Spectrometric analysis of microbes

ABSTRACT

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed. The method comprises: using a first device to generate smoke, aerosol or vapour from a target comprising or consisting of a microbial population; mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and analysing said spectrometric data in order to analyse said microbial population.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 17/531,490, filed Nov. 19, 2021, which is acontinuation of U.S. patent application Ser. No. 15/556,022, filed Sep.6, 2017, which is the U.S. National Phase of International ApplicationNo. PCT/GB2016/050610, filed Mar. 7, 2016, which claims priority fromand the benefit of United Kingdom patent application No. 1503876.3 filedon Mar. 6, 2015, United Kingdom patent application No. 1503864.9 filedon Mar. 6, 2015, United Kingdom patent application No. 1518369.2 filedon Oct. 16, 2015, United Kingdom patent application No. 1503877.1 filedon Mar. 6, 2015, United Kingdom patent application No. 1503867.2 filedon Mar. 6, 2015, United Kingdom patent application No. 1503863.1 filedon Mar. 6, 2015, United Kingdom patent application No. 1503878.9 filedon Mar. 6, 2015, United Kingdom patent application No. 1503879.7 filedon Mar. 6, 2015 and United Kingdom patent application No. 1516003.9filed on Sep. 9, 2015. The entire contents of these applications areincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to mass spectrometry and/or ion mobilityspectrometry and in particular to methods of detecting, identifyingand/or characterising microbes and/or compounds produced by microbes.

BACKGROUND

Traditional methods of microbial detection or identification rely onculture-based detection, followed by phenotypic identification of themicrobe using microscopic analysis, Gram staining, culture and/orbiochemical assays to detect certain biomarkers. It is estimated thatonly about 1% of all microorganisms can be cultured, so culture-basedapproaches have significant drawbacks.

Although not a taxonomical classification, the Gram strain of bacteriais a widely used classification system for bacteria, especially inclinical microbiological practice. Using Gram-staining, bacteria cangenerally be subdivided into two groups, Gram-positive and Gram-negativestaining bacteria. The Gram-staining behaviour is determined by thecapabilities of the peptidoglycan layer of the bacterial cell wall toretain the crystal violet dye. Gram-negative bacteria are encased by aninner cell membrane, a cell wall comprising peptidoglycan, and an outercell membrane, whereas Gram-positive bacteria lack the outer cellmembrane. Gram-stains are of significant importance in clinicalmicrobiology settings because Gram-positives generally are moresusceptible for antibiotics due to the lack of the outer cell membrane.

More recent methods typically involve molecular biology analysistechniques such as nucleic acid analysis using restriction enzymes,hybridisation, polymerase chain reaction (PCR) amplification and/orsequencing. These methods generally need extensive and careful samplepreparation, are comparably expensive and still need at least severalhours for identification. Due to these reasons, sequencing methods arerarely applied in routine clinical settings.

Bacterial species are typically defined by their 16S rRNA sequence, thussequencing of the 16S rRNA encoding gene serves as the gold standard forbacterial identification and classification. Partial or full 16S rRNAsequencing has the advantage of being culture-independent and thus isespecially valuable for fastidious microorganisms. However, thesensitivity and specificity for direct sample applications variesconsiderably. Moreover, in some cases bacterial species cannot beconfidently identified by their 16S rRNA sequence, necessitating theapplication of additional techniques such as sequencing of further genetargets.

Bacteria are reliant on their cell envelope to protect them. Thebacterial cell envelope is a complex structure comprising at least onephospholipid bilayer. The survival of bacteria depends especially onmembrane lipid homeostasis and the ability to adjust lipid compositionto adapt the bacterial cell to different environments. Most membranephospholipids are glycerolipids that contain two fatty acid chains.These phospholipid acyl chains determine the viscosity of the membrane,which in turn influences many crucial membrane-associated functions,such as the passive permeability of hydrophobic molecules, active solutetransport and protein-protein interactions.

The most commonly encountered phosphatidylglycerol lipids arephosphatidic acids (PAs), phosphatidylethanolamines (PEs),phosphatidylglycerols (PGs), phosphatidylcholines (PCs),phosphatidylinositols (PIs) and phosphatidylserines (PSs). They share acommon phosphatidylglycerophosphate backbone but differ in the chemicalnature of their respective headgroups. Two fatty acids are attached tothe glycerol backbone in sn1- and sn2-position and usually are presentin various chain lengths of between 14 and 20 carbons. Many of thesefatty acids contain trans double bonds, a single cis double bond, or isoor anteiso methyl branches. The formation of cyclopropane rings bymethylation of cis double bonds is another commonly occurringmodification among bacteria. Bacteria have evolved a number of differentmechanisms to control the de-novo formation of fatty acids and modifythe structure of existing fatty acids in order to adjust membraneviscosity as required.

Another available technique is fatty acid profiling of microbes, such asbacteria, using gas-chromatography coupled to a flame ionisationdetector (GC-FID).

Mass spectrometry-based identification of microorganisms has been shownto be applicable to some microorganisms. For example, matrix-assistedlaser desorption ionisation time-of-flight mass spectrometry(MALDI-TOF-MS) can provide microbial identifications.

Knowledge of the identity of a pathogen causing disease facilitatesadequate medical treatment, and, e.g., in clinical settings, informationabout certain characteristics of a microbe can be particularly useful.For example, knowledge regarding the susceptibility or resistance of amicrobe to an antibiotic or other drug can help to guide treatmentdecisions. A quick decision on the most suitable antibiotic treatmentcan significantly shorten the duration of the infection and in somecases, such as meningitides or sepsis, potentially be life-saving.

Extensive research was performed using MALDI-TOF-MS to address this andother shortcomings in subspecies typing and led to solutions beingdeveloped for some of these problems such as the detection ofβ-lactamase activity to determine susceptibility to β-lactamantibiotics. However, these solutions require additionalsample-preparation and culturing steps which are time-consuming.Moreover, only a small subset of the proteins present within a microbecan be detected with MALDI-MS measurements. It was shown that theproteins detected from whole cells by MALDI share the properties of highabundance, strong basicity, and medium hydrophobicity. In the case of E.coli, all of the detected proteins originated from the cell interior,with about half of those coming from the ribosome.

A major drawback of routine MALDI-TOF-MS protocols involving bacterialprotein profiling is that it is not directly applicable to humansamples, because the human protein background and low bacterial countscomplicate bacterial detection. It is also not particularly suitable foranalysing microbial mixtures, as the components of microbial mixturestypically cannot be reliably identified.

Thus, there is still an unmet need for a microbial identification methodthat would lead to taxonomic information, such as species-levelinformation, while ideally simultaneously providing information onmicrobial phenotypes, such as information that would allow selection ofan adequate antimicrobial treatment.

It is desired to provide a method of analysis, e.g., detection,identification and/or characterisation of microbes and/or compoundsproduced by microbes.

SUMMARY

The present invention relates generally to the application of massspectrometry and/or ion mobility spectrometry to analyse a sample whichmay comprise microbes and/or compounds produced by microbes.

The invention provides a method of analysis using mass spectrometryand/or ion mobility spectrometry comprising:

(a) using a first device to generate smoke, aerosol or vapour from atarget comprising or consisting of a microbial population;

(b) mass analysing and/or ion mobility analysing said smoke, aerosol orvapour, or ions derived therefrom, in order to obtain spectrometricdata; and

(c) analysing said spectrometric data in order to analyse said microbialpopulation.

Various embodiments are contemplated wherein analyte ions are generatedfrom the target, smoke, aerosol or vapour, e.g., by an ambientionisation ion source. The analyte ions, or ions derived therefrom, maybe subjected either to: (i) mass analysis by a mass analyser such as aquadrupole mass analyser or a Time of Flight mass analyser; (ii) ionmobility analysis (IMS) and/or differential ion mobility analysis (DMA)and/or Field Asymmetric Ion Mobility Spectrometry (FAIMS) analysis;and/or (iii) a combination of firstly ion mobility analysis (IMS) and/ordifferential ion mobility analysis (DMA) and/or Field Asymmetric IonMobility Spectrometry (FAIMS) analysis followed by secondly massanalysis by a mass analyser such as a quadrupole mass analyser or a Timeof Flight mass analyser (or vice versa). Various embodiments also relateto an ion mobility spectrometer and/or mass analyser and a method of ionmobility spectrometry and/or method of mass analysis.

Embodiments of the methods provided herein are discussed in the detaileddescription.

Optional features of the methods are discussed below. Thus, unlessotherwise stated, any reference to “a method” or “the method” isintended to be a reference to any of the provided methods listed herein.It is explicitly intended that any of these features may be present inany combination in any of these methods.

Also provided is a method of ion imaging comprising: automaticallysampling using a rapid evaporation ionization mass spectrometry(“REIMS”) device a plurality of different locations of a bacterialand/or a fungal sample which has been cultured on to a culture medium;obtaining spectrometric data corresponding to each said location; andusing said obtained spectrometric data to identify one or more bacterialstrains and/or one or more fungal strains at each said location.

Optionally, said culture medium comprises an agar-based medium, acarbohydrate matrix or another solid growth medium.

Optionally, said method further comprises determining the spatialdistribution of one or more excreted substances emanating from one ormore bacterial colonies and/or fungal colonies which have been culturedon said medium.

Optionally, said one or more excreted substances is selected from thegroup consisting of: (i) one or more metabolites; (ii) one or moreprimary metabolites; (iii) one or more secondary metabolites; (iv) oneor more lipopeptides; (v) surfactin; (vi) one or more quorum sensingmolecules; (vii) 2-Heptyl-3-hydroxy-4(1H)-quinolone or2-heptyl-3,4-dihydroxyquinoline (“PQS” or Pseudomonas quinolone signal);(viii) 4-hydroxy-2-heptylquinoline (“HHQ”); (ix) one or moreantibiotics; (x) one or more alkaloids; (xi) one or more terpenoids;(xii) one or more glycosides; (xiii) one or more natural phenols; (xiv)one or more phenazines; (xv) one or more biphenyls and dibenzofurans;(xvi) one or more beta-lactams; (xvii) one or more polyketides; (xviii)one or more fatty acid synthase products; (xix) one or more nonribosomalpeptides; and (xx) one or more ribosomal peptides.

Optionally, the step of automatically sampling a plurality of differentlocations of a bacterial and/or fungal sample comprises sampling using adisposable tip.

Also provided is an ion imager comprising:

a rapid evaporation ionization mass spectrometry (“REIMS”) device whichis arranged to automatically sample a plurality of different locationsof a bacterial and/or a fungal sample which has been cultured on to aculture medium; and

a mass and/or ion mobility analyser arranged and adapted: (i) to obtainspectrometric data corresponding to each said location; and (ii) to usesaid obtained spectrometric data to identify one or more bacterialstrains and/or one or more fungal strains at each said location.

Optionally, said culture medium comprises an agar-based medium, acarbohydrate matrix or another solid growth medium.

Optionally, said ion imager is arranged and adapted to determine thespatial distribution of one or more excreted substances emanating fromone or more bacterial colonies and/or fungal colonies which have beencultured on said medium.

Optionally, said one or more excreted substances is selected from thegroup consisting of: (i) one or more metabolites; (ii) one or moreprimary metabolites; (iii) one or more secondary metabolites; (iv) oneor more lipopeptides; (v) surfactin; (vi) one or more quorum sensingmolecules; (vii) 2-Heptyl-3-hydroxy-4(1H)-quinolone or2-heptyl-3,4-dihydroxyquinoline (“PQS” or Pseudomonas quinolone signal);(viii) 4-hydroxy-2-heptylquinoline (“HHQ”); (ix) one or moreantibiotics; (x) one or more alkaloids; (xi) one or more terpenoids;(xii) one or more glycosides; (xiii) one or more natural phenols; (xiv)one or more phenazines; (xv) one or more biphenyls and dibenzofurans;(xvi) one or more beta-lactams; (xvii) one or more polyketides; (xviii)one or more fatty acid synthase products; (xix) one or more nonribosomalpeptides; and (xx) one or more ribosomal peptides.

Optionally, said ion imager is arranged and adapted to use a disposabletip to automatically sampling a plurality of different locations of abacterial and/or a fungal sample.

Also provided is a method of Rapid Evaporation Ionization MassSpectrometry (“REIMS”) comprising:

using a REIMS ionisation source to analyse a biological liquid for thepresence or absence of bacteria in said biological liquid.

Optionally, said biological liquid is selected from the group consistingof: (i) blood; (ii) urine; (iii) saliva; (iv) sputum; or (v) serum.

Optionally, said method further comprises using a disposable samplingtip to sample said biological liquid.

Optionally, said method further comprises aspirating or passing saidbiological liquid through a filter media.

Optionally, said method further comprises analysing residue on saidfilter media which remains after said biological liquid has beenaspirated or passed through said filter media.

Obtaining the spectrometric data may comprise recording the ion signalintensity of the ions derived from the smoke, aerosol or vapour as afunction of one or more physicochemical property (or as a function of aproperty related thereto). For example, the ion signal intensity may berecorded as a function of mass to charge ratio and/or ion mobility. Thelocation and/or size and/or pattern of peaks in this recorded ion signalmay then be used to characterise or identify one or more analytespresent in the smoke, aerosol or vapour.

Tandem mass spectrometry may be used to assign an analyte/compound toeach of the peaks. For example, parent ions having a physicochemicalproperty (e.g., mass to charge ratio) corresponding to that of a peakmay be isolated (e.g., using a mass filter) and then fragmented orreacted so as to produce fragment or product ions. These fragment orproduct ions may then be analysed (e.g., by mass analysis) and theirdetermined properties used to identify the parent ion giving rise to thepeak in the ion signal. Such tandem mass spectrometry may be used, forexample, to identify biomarkers in the spectrometric data.

The mass and/or ion mobility spectrometer may obtain data in negativeion mode only, positive ion mode only, or in both positive and negativeion modes. Positive ion mode spectrometric data may be combined orconcatenated with negative ion mode spectrometric data. Negative ionmode can provide particularly useful spectra for classifying aerosol,smoke or vapour samples, such as aerosol, smoke or vapour samples fromtargets comprising lipids.

Ion mobility spectrometric data may be obtained using different ionmobility drift gases, or dopants may be added to the drift gas to inducea change in drift time of one or more species. This data may then becombined or concatenated.

Also provided is an apparatus comprising:

a Rapid Evaporation Ionization Mass Spectrometry (“REIMS”) device whichis arranged and adapted to analyse a biological liquid for the presenceor absence of bacteria in said biological liquid.

Optionally, said biological liquid is selected from the group consistingof: (i) blood; (ii) urine; (iii) saliva; (iv) sputum; or (v) serum.

Optionally, said apparatus further comprises a disposable sampling tipto sample said biological liquid.

Optionally, said apparatus further comprises a device which is arrangedand adapted to aspirate or pass said biological liquid through a filtermedia.

Optionally, said apparatus further comprises an analyser which isarranged and adapted to analyse residue on said filter media whichremains after said biological liquid has been aspirated or passedthrough said filter media.

Also provided is a method comprising:

obtaining an optical image of a substrate and determining on the basisof said optical image if one or more areas of interest exist on saidsubstrate;

wherein if one or more areas of interest are determined to exist, thensaid method further comprises the steps of:

(i) automatically sampling at least one location within at least onedetermined area of interest using a rapid evaporation ionization massspectrometry (“REIMS”) device and obtaining spectrometric datacorresponding to said at least one location; and

(ii) using said obtained spectrometric data to identify one or morebacterial strains and/or one or more fungal strains at said one or morelocations.

Optionally, said substrate comprises a food product.

Also provided is an apparatus comprising:

a rapid evaporation ionization mass spectrometry (“REIMS”) device;

a device arranged and adapted to obtain an optical image of a substrate;and a control system arranged and adapted:

(i) to determine on the basis of said optical image if one or more areasof interest exist on said substrate, wherein if one or more areas ofinterest are determined to exist, then said control system is furtherarranged and adapted to:

(ii) to automatically sample at least one location within at least onedetermined area of interest using said rapid evaporation ionization massspectrometry (“REIMS”) device and to obtain spectrometric datacorresponding to said at least one location; and

(iii) to use said obtained spectrometric data to identify one or morebacterial strains and/or one or more fungal strains at said one or morelocations.

Optionally, said substrate comprises a food product.

Also provided is a method of ion imaging comprising:

dispensing a bacterial and/or fungal sample onto a culture medium,wherein one or more antibiotic and/or antifungal substances are embeddedwithin and/or on said culture medium; automatically sampling using arapid evaporation ionization mass spectrometry (“REIMS”) device aplurality of different locations of said bacterial and/or a fungalsample which has been cultured on said culture medium; obtainingspectrometric data corresponding to each said location; and determiningfrom said spectrometric data information concerning the resistance orotherwise of said sample to said one or more antibiotic and/orantifungal substances.

Also provided is an ion imager comprising: a rapid evaporationionization mass spectrometry (“REIMS”) device; and a control systemarranged and adapted:

-   (i) to automatically sample using said rapid evaporation ionization    mass spectrometry (“REIMS”) device a plurality of different    locations of a bacterial and/or a fungal sample which has been    cultured on a culture medium, wherein one or more antibiotic and/or    antifungal substances are embedded within and/or on said culture    medium;-   (ii) to obtain spectrometric data corresponding to each said    location; and-   (iii) to determine from said spectrometric data information    concerning the resistance or otherwise of said sample to said one or    more antibiotic and/or antifungal substances.

Numerous different applications are contemplated and these will now bedescribed in greater detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will now be described, by way of example only, andwith reference to the accompanying drawings in which:

FIG. 1 shows an embodiment wherein a REIMS imaging platform is locatedabove a sample, e.g., tissue sample to be imaged;

FIG. 2 shows a workflow of a combined DESI and REIMS imaging platformanalysis for co-registration of histological features between an opticalimage and DESI and REIMS data;

FIG. 3 shows a heated coil interface used on a Waters Xevo G2-S®instrument for improved sensitivity and robustness towardscontamination;

FIG. 4 shows a setup of REIMS imaging instrumentation;

FIG. 5 shows a REIMS imaging sampling probe and setup of a xyz-stagewherein a sampling probe is mounted onto a z-actuator and is connectedto a high voltage power supply and wherein evaporated aerosol iscaptured by suction tubing and is transported to a mass and/or ionmobility spectrometer;

FIG. 6 shows an example mass spectrum of P. aeruginosa bacterium withmost prominent metabolite classes for distinct mass ranges;

FIG. 7 shows optical, mass spectrometric multivariate and ion images ofthree different bacterial species wherein the multivariate image showsclear distinction between the species, while ion images show metabolitesof current interest, including phospholipids. Molecules were ionized as[M-H] wherein PA: phosphatidic acid, PG: phosphatidyl-glycerol, PQS:2-Heptyl-3hydroxy-4(1H)-quinolone;

FIG. 8 shows a principal components analysis plot of three differentbacterial strains together with agar medium and wherein PC is theprincipal component and percentage values are explained variance;

FIG. 9 shows mean mass spectra and mean phospholipid-class intensitylevels for each lipid species wherein mean intensities of phospholipidclasses are stable across the species, with highest level for PA classand lowest level for PG class and wherein PA: phosphatidic acid, PE:phosphatidyl-ethanolamine, PG: phosphatidyl-glycerol and wherein n(P.aeruginosa)=48, n(B. subtilis)=45, n(S. aureus)=52;

FIGS. 10A-10C show an experimental setup used for REIMS analysis of atarget comprising or consisting of a microbial population which may beused in a method provided herein provided herein;

FIG. 11 shows an interface for ionizing aerosol from the targetcomprising or consisting of a microbial population;

FIGS. 12A-12B show a DESI method for analyzing a target comprising orconsisting of a microbial population;

FIG. 13 shows a fragmentation spectrum and scheme of fragmentation foran oxidised ceramide signal at m/z 590 obtained from B. fragilis;

FIG. 14 shows fragmentation spectra obtained for peaks at m/z 932, 946and 960 from Parabacteroides distonasis assigned as C15:0 substitutedphosphoglycerol dihydroceramides (subPG-DHC);

FIG. 15 shows a method of analysis that comprises building aclassification model according to various embodiments;

FIG. 16 shows a set of reference sample spectra obtained from twoclasses of known reference samples;

FIG. 17 shows a multivariate space having three dimensions defined byintensity axes, wherein the multivariate space comprises pluralreference points, each reference point corresponding to a set of threepeak intensity values derived from a reference sample spectrum;

FIG. 18 shows a general relationship between cumulative variance andnumber of components of a PCA model;

FIG. 19 shows a PCA space having two dimensions defined by principalcomponent axes, wherein the PCA space comprises plural transformedreference points or scores, each transformed reference point or scorecorresponding to a reference point of FIG. 17 ;

FIG. 20 shows a PCA-LDA space having a single dimension or axis, whereinthe LDA is performed based on the PCA space of FIG. 19 , the PCA-LDAspace comprising plural further transformed reference points or classscores, each further transformed reference point or class scorecorresponding to a transformed reference point or score of FIG. 19 ;

FIG. 21 shows a method of analysis that comprises using a classificationmodel according to various embodiments;

FIG. 22 shows a sample spectrum obtained from an unknown sample;

FIG. 23 shows the PCA-LDA space of FIG. 20 , wherein the PCA-LDA spacefurther comprises a PCA-LDA projected sample point derived from the peakintensity values of the sample spectrum of FIG. 22 ;

FIG. 24 shows a method of analysis that comprises building aclassification library according to various embodiments;

FIG. 25 shows a method of analysis that comprises using a classificationlibrary according to various embodiments;

FIG. 26A shows PCA analysis of 7 Candida species using the methodprovided herein with REIMS technology; and FIG. 26B shows LDA analysisof 7 Candida species using the method provided herein with REIMStechnology;

FIG. 27A shows spectral profiles of a pure Escherichia coli isolate;FIG. 27B shows spectral profiles of a pure Candida albicans isolate; andFIG. 27C shows spectral profiles of an amalgamated sample containing anequal ratio of the two, wherein isolate specific peaks are highlighted;

FIG. 28 shows LDA analysis of spectrometric data obtained using anembodiment of the method provided herein with REIMS with C. difficileribotyped isolates;

FIG. 29 shows LDA and cross validation analysis of MRSA and MSSAisolates;

FIG. 30 shows spectrometric data obtained using an embodiment of themethod provided herein with REIMS for five different clinical isolatesof each of Staphylococcus aureus (top), Pseudomonas aeruginosa (middle)and Escherichia coli (bottom) cultured on Columbia blood agar;

FIGS. 31A-31D show zoomed regions of mass spectra of S. aureus grownunder aerobic (A and C) and anaerobic (C and D) conditions. A+B) m/z650-800, C+D) m/z 1250-1750;

FIG. 32 shows zoomed spectrometric data obtained using the methodprovided herein provided herein with REIMS for five differentCorynebacterium species, which reveals different mycolic acid patterns;spectra normalised to base peak;

FIG. 33 shows the general workflow applied for finding taxon-specificmarkers;

FIG. 34 Shows spectrometric data obtained using an embodiment of themethod provided herein with REIMS for a clinical isolate of Pseudomonasaeruginosa grown as a lawn (top) and as single colonies (bottom), withspectrometric data for quorum sensing molecules and rhamnolipidshighlighted;

FIG. 35 shows fragmentation spectra for rhamnolipids species detected inPseudomonas aeruginosa recorded using Thermo LTQ XL ion trap instrumentusing collision induced dissociation at collision energy setting of 25;

FIG. 36 shows peak intensities for m/z=747.52 in mycoplasma infected andmycoplasma-free cell lines during the duration of the Plasmocin®treatment wherein day 1 corresponds with the original (Mycoplasmapositive or negative) sample, day 2 corresponds with the addition ofPlasmocin®, day 3 corresponds with Plasmocin® still being present, day 4corresponds with the removal of Plasmocin® and wherein day 5 correspondswith all samples being Mycoplasma-free;

FIG. 37A shows a number of significantly higher m/z signals inMycoplasma-infected versus Mycoplasma-free samples in HEK and HeLa celllines. For FIG. 37B Mycoplasma-infected (+) and Mycoplasma-free (−) HEK(rectangle) and HeLa (triangle) cells were either treated (t) oruntreated (u). Samples are shown as a function of PC1 and PC2 of PCAtransformed samples in the space of the 18 overlapping m/z signals;

FIGS. 38A and 38B show intensities of TIC normalised and log-transformedsignals at m/z=819.52 (corresponding to PG(40:7)) in mycoplasma-free,mycoplasma-infected and Plasmocin™ treated samples in HeLa (FIG. 38A)and HEK cell lines (FIG. 38B);

FIGS. 39A-B show the tissue type-distribution of a cancerous tissuespecimen that originated from the centre of tumour dissected during aright hemicolectomy. FIG. 39A is a DESI-MS image displaying tissue typedistribution in a colorectal tissue specimen; the original image showedtumour tissue in green and stroma tissue in red. On the black and whiteFigure, tumour tissue is light grey and stroma tissue is darker grey.FIG. 39B is a H&E stained and histopathologically annotated sectionpost-DESI;

FIG. 40 shows full scan mass spectra for colorectal adenocarcinoma,tumour surrounding stroma and necrotic tissue of same tissue sectionshown in FIG. 39 . Stars indicate major taxonomic markers;

FIG. 41 shows single ion images and representative intensitydistribution plots for known and confirmed homologous sphingolipidspecies that showed specificity as taxonomic markers; a signalcorresponding to m/z=705.5562 indicates the presence of a ceramidephosphorylethanolamine, which in turn indicates a member of thebacteriodetes class, as shown on the right; a signal corresponding tom/z=618.5233 indicates a hydroxylated ceramide, which in turn indicatesa member of the bacteriodetes class, as shown on the right; a signalcorresponding to m/z=946.7472 indicates an Iso-C15:0-substitutedphosphoglycerol dihydroceramide, which in turn is indicative of a memberof the Porphyromonadacae family;

FIG. 42 shows single ion images and intensity selected distributionplots for other taxonomical markers a signal corresponding tom/z=653.5113 is indicative of a member of the Bacteriodetes phylum, asshown on the right; a signal corresponding to m/z=566.4794 is indicativeof a member of the Flavobacteria class, as shown on the right; a signalcorresponding to m/z=731.5253 is indicative of a member of theClostridiaceae family; and a signal corresponding to m/z=646.4833 isindicative of a member of the Fusobacteria phylum, as shown on theright;

FIG. 43A shows desorption electrospray ionisation (“DESI”) spectrometricanalysis of a microbial sample on a swab in accordance with variousembodiments and shows that microbial samples can be detected using DESI,and FIG. 43B shows a comparison with rapid evaporative ionisation massspectrometry (“REIMS”) analysis in conjunction with a Time of Flightmass analysis of a microbial sample directly from an agar plate;

FIG. 44A shows averaged desorption electrospray ionisation (“DESI”) massspectra of diverse analysed microorganism species including Candidaalbicans, Pseudomonas montelli, Staphylococcus epidermis, Moraxellacatarrhalis, Klebsiella pneumonia and Lactobacillus sp as well aspregnant vaginal mucosa, and FIGS. 44B and 44C show PCA plots showing aseparation between the vaginal mucosa (pregnant and non-pregnant group)from the microorganism species within the first two components, and aseparation between the different bacteria and fungi species.

DETAILED DESCRIPTION

Although the methods provided herein have been described with referenceto preferred embodiments, it will be understood by those skilled in theart that various changes in form and detail may be made withoutdeparting from the scope of the invention as set forth in theaccompanying claims.

Mass spectrometry (“MS”) based identification techniques such as ambientionization mass spectrometry are known. Direct ambient ionization massspectrometry, such as REIMS, has emerged as a technology allowingreal-time analysis of targets.

The method provided herein may, for example, be used in or with areal-time, robust characterisation tool which utilises ambientionisation technologies, such as REIMS.

Various embodiments are described in more detail below which in generalrelate to generating smoke, aerosol or vapour from a target (details ofwhich are provided elsewhere herein) using an ambient ionization ionsource. The aerosol, smoke or vapour may then be mixed with a matrix andaspirated into a vacuum chamber of a mass and/or ion mobilityspectrometer. The mixture may be caused to impact upon a collisionsurface causing the aerosol, smoke or vapour to be ionized by impactionization which results in the generation of analyte ions. Theresulting analyte ions (or fragment or product ions derived from theanalyte ions) may then be mass and/or ion mobility analysed and theresulting mass and/or ion mobility spectrometric data may be subjectedto multivariate analysis or other mathematical treatment in order todetermine one or more properties of the target in real time.

Ambient Ionization Ion Sources

In any of the methods provided herein a device may be used to generatean aerosol, smoke or vapour from one or more regions of a target(details of which are provided elsewhere herein). The device maycomprise an ambient ionization ion source which is characterised by theability to generate analyte aerosol, smoke or vapour from target,optionally with little or no preparation of the target for analysis. Bycontrast, other types of ionization ion sources such as Matrix AssistedLaser Desorption Ionisation (“MALDI”) ion sources require a matrix orreagent to be added to the sample prior to ionization.

It will be apparent that the requirement to add a matrix or a reagentdirectly to a sample may prevent the ability to perform in vivo analysisof tissue and also, more generally, prevents the ability to provide arapid simple analysis of target material.

Ambient ionization techniques are particularly useful since they enablea rapid simple analysis of target material to be performed. Whilst thereis no requirement to add a matrix or reagent to a sample in order toperform ambient ionization techniques, the method may optionally includea step of adding a matrix or reagent to the target (e.g., directly tothe target) prior to analysis. The matrix or reagent may be added to thetarget, e.g., to lyse the cells of the target or to enhance the signaltherefrom during the analysis.

A number of different ambient ionization techniques are known and areintended to fall within the scope of the present invention. As a matterof historical record, Desorption Electrospray Ionisation (“DESI”) wasthe first ambient ionization technique to be developed and was disclosedin 2004. Since 2004, a number of other ambient ionization techniqueshave been developed. These ambient ionization techniques differ in theirprecise ionization method but they share the same general capability ofgenerating gas-phase ions directly from samples (e.g., withoutpreparation of the sample for analysis). The various ambient ionizationtechniques which are intended to fall within the scope of the presentinvention may not require any sample preparation for the analysis. As aresult, the various ambient ionization techniques enable targets to beanalysed without the time, expense and problems associated with adding amatrix or reagent to the target material.

A list of ambient ionization techniques which are intended to fallwithin the scope of the present invention are given in the followingtable:

Acronym Ionisation technique DESI Desorption electrospray ionisationDeSSI Desorption sonic spray ionisation DAPPI Desorption atmosphericpressure photoionisation EASI Easy ambient sonic-spray ionisation JeDIJet desorption electrospray ionisation TM-DESI Transmission modedesorption electrospray ionisation LMJ-SSP Liquid microjunction-surfacesampling probe DICE Desorption ionisation by charge exchange Nano-Nanospray desorption electrospray ionisation DESI EADESIElectrode-assisted desorption electrospray ionisation APTDCI Atmosphericpressure thermal desorption chemical ionisation V-EASI Venturi easyambient sonic-spray ionisation AFAI Air flow-assisted ionisation LESALiquid extraction surface analysis PTC-ESI Pipette tip columnelectrospray ionisation AFADESI Air flow-assisted desorptionelectrospray ionisation DEFFI Desorption electro-flow focusingionisation ESTASI Electrostatic spray ionisation PASIT Plasma-basedambient sampling ionisation transmission DAPCI Desorption atmosphericpressure chemical ionisation DART Direct analysis in real time ASAPAtmospheric pressure solid analysis probe APTDI Atmospheric pressurethermal desorption ionisation PADI Plasma assisted desorption ionisationDBDI Dielectric barrier discharge ionisation FAPA Flowing atmosphericpressure afterglow HAPGDI Helium atmospheric pressure glow dischargeionisation APGDDI Atmospheric pressure glow discharge desorptionionisation LTP Low temperature plasma LS-APGD Liquidsampling-atmospheric pressure glow discharge MIPDI Microwave inducedplasma desorption ionisation MFGDP Microfabricated glow discharge plasmaRoPPI Robotic plasma probe ionisation PLASI Plasma spray ionisationMALDESI Matrix assisted laser desorption electrospray ionisation ELDIElectrospray laser desorption ionisation LDTD Laser diode thermaldesorption LAESI Laser ablation electrospray ionisation CALDI Chargeassisted laser desorption ionisation LA-FAPA Laser ablation flowingatmospheric pressure afterglow LADESI Laser assisted desorptionelectrospray ionisation LDESI Laser desorption electrospray ionisationLEMS Laser electrospray mass spectrometry LSI Laser spray ionisationIR-LAMICI Infrared laser ablation metastable induced chemical ionisationLDSPI Laser desorption spray post-ionisation PAMLDI Plasma assistedmultiwavelength laser desorption ionisation HALDI High voltage-assistedlaser desorption ionisation PALDI Plasma assisted laser desorptionionisation ESSI Extractive electrospray ionisation PESI Probeelectrospray ionisation ND-ESSI Neutral desorption extractiveelectrospray ionisation PS Paper spray DIP-APCI Direct inletprobe-atmospheric pressure chemical ionisation TS Touch spray Wooden-tipWooden-tip electrospray CBS-SPME Coated blade spray solid phasemicroextraction TSI Tissue spray ionisation RADIO Radiofrequencyacoustic desorption ionisation LIAD-ESI Laser induced acousticdesorption electrospray ionisation SAWN Surface acoustic wavenebulization UASI Ultrasonication-assisted spray ionisation SPA- Solidprobe assisted nanoelectrospray ionisation nanoESI PAUSI Paper assistedultrasonic spray ionisation DPESI Direct probe electrospray ionisationESA-Py Electrospray assisted pyrolysis ionisation APPIS Ambient pressurepyroelectric ion source RASTIR Remote analyte sampling transport andionisation relay SACI Surface activated chemical ionisation DEMIDesorption electrospray metastable-induced ionisation REIMS Rapidevaporative ionisation mass spectrometry SPAM Single particle aerosolmass spectrometry TDAMS Thermal desorption-based ambient massspectrometry MAII Matrix assisted inlet ionisation SAII Solvent assistedinlet ionisation SwiFERR Switched ferroelectric plasma ioniser LPTDLeidenfrost phenomenon assisted thermal desorption

According to an embodiment the ambient ionization ion source maycomprise a rapid evaporative ionization mass spectrometry (“REIMS”) ionsource wherein a RF voltage is applied to one or more electrodes inorder to generate smoke, aerosol or vapour by Joule heating.

However, it will be appreciated that other ambient ion sources includingthose referred to above may also be utilised. For example, according toanother embodiment the ambient ionization ion source may comprise alaser ionization ion source. According to an embodiment the laserionisation ion source may comprise a mid-IR laser ablation ion source.For example, there are several lasers which emit radiation close to orat 2.94 μm which corresponds with the peak in the water absorptionspectrum. According to various embodiments the ambient ionization ionsource may comprise a laser ablation ion source having a wavelengthclose to 2.94 μm on the basis of the high absorption coefficient ofwater at 2.94 μm. According to an embodiment the laser ablation ionsource may comprise a Er:YAG laser which emits radiation at 2.94 μm.

Other embodiments are contemplated wherein a mid-infrared opticalparametric oscillator (“OPO”) may be used to produce a laser ablationion source having a longer wavelength than 2.94 μm. For example, anEr:YAG pumped ZGP-OPO may be used to produce laser radiation having awavelength of, e.g., 6.1 μm, 6.45 μm or 6.73 μm. In some situations itmay be advantageous to use a laser ablation ion source having a shorteror longer wavelength than 2.94 μm since only the surface layers will beablated and less thermal damage may result. According to an embodiment aCo:MgF₂ laser may be used as a laser ablation ion source wherein thelaser may be tuned from 1.75-2.5 μm. According to another embodiment anoptical parametric oscillator (“OPO”) system pumped by a Nd:YAG lasermay be used to produce a laser ablation ion source having a wavelengthbetween 2.9-3.1 μm. According to another embodiment a CO₂ laser having awavelength of 10.6 μm may be used to generate the aerosol, smoke orvapour.

According to other embodiments the ambient ionization ion source maycomprise an ultrasonic ablation ion source, or a hybridelectrosurgical-ultrasonic ablation source, that generates a liquidsample which is then aspirated as an aerosol. The ultrasonic ablationion source may comprise a focused or unfocussed ultrasound.

According to an embodiment the first device for generating aerosol,smoke or vapour from the target may comprise a tool which utilises an RFvoltage, such as a continuous RF waveform. According to otherembodiments a radiofrequency system may be used which is arranged tosupply pulsed plasma RF energy to a tool. The tool may comprise, forexample, a PlasmaBlade®. Pulsed plasma RF tools operate at lowertemperatures than conventional electrosurgical tools (e.g., 40-170° C.c.f. 200-350° C. ) thereby reducing thermal damage depth. Pulsedwaveforms and duty cycles may be used for both cut and coagulation modesof operation by inducing electrical plasma along the cutting edge(s) ofa thin insulated electrode.

According to an embodiment the first device comprises a surgicalwater/saline jet device such as a resection device, a hybrid of suchdevice with any of the other devices herein, an electrosurgery argonplasma coagulation device, a hybrid argon plasma coagulation andwater/saline jet device.

Other embodiments are contemplated wherein the first device forgenerating aerosol, smoke or vapour from the target may comprise anargon plasma coagulation (“APC”) device. An argon plasma coagulationdevice involves the use of a jet of ionised argon gas (plasma) that isdirected through a probe. The probe may be passed through an endoscope.Argon plasma coagulation is essentially a non-contact process as theprobe is placed at some distance from the target. Argon gas is emittedfrom the probe and is then ionized by a high voltage discharge (e.g., 6kV). High-frequency electric current is then conducted through the jetof gas, resulting in coagulation of the target on the other end of thejet. The depth of coagulation is usually only a few millimetres.

The first device, surgical or electrosurgical tool, device or probe orother sampling device or probe disclosed in any of the aspects orembodiments herein may comprise a non-contact surgical device, such asone or more of a hydrosurgical device, a surgical water jet device, anargon plasma coagulation device, a hybrid argon plasma coagulationdevice, a water jet device and a laser device.

A non-contact surgical device may be defined as a surgical devicearranged and adapted to dissect, fragment, liquefy, aspirate, fulgurateor otherwise disrupt biologic tissue without physically contacting thetissue. Examples include laser devices, hydrosurgical devices, argonplasma coagulation devices and hybrid argon plasma coagulation devices.

As the non-contact device may not make physical contact with the tissue,the procedure may be seen as relatively safe and can be used to treatdelicate tissue having low intracellular bonds, such as skin or fat.

According to the various embodiments of the invention, the first devicecomprises or forms part of an ambient ion or ionisation source; or saidfirst device generates said aerosol, smoke or vapour from the target andcontains ions and/or is subsequently ionised by an ambient ion orionisation source, or other ionisation source.

Optionally, the first device comprises or forms part of a device, or anion source selected from the group consisting of: (i) a rapidevaporative ionisation mass spectrometry (“REIMS”) ion source; (ii) adesorption electrospray ionisation (“DESI”) ion source; (iii) a laserdesorption ionisation (“LDI”) ion source; (iv) a thermal desorption ionsource; (v) a laser diode thermal desorption (“LDTD”) ion source; (vi) adesorption electro-flow focusing (“DEFFI”) ion source; (vii) adielectric barrier discharge (“DBD”) plasma ion source; (viii) anAtmospheric Solids Analysis Probe (“ASAP”) ion source; (ix) anultrasonic assisted spray ionisation ion source; (x) an easy ambientsonic-spray ionisation (“EAST”) ion source; (xi) a desorptionatmospheric pressure photoionisation (“DAPPI”) ion source; (xii) apaperspray (“PS”) ion source; (xiii) a jet desorption ionisation(“JeDI”) ion source; (xiv) a touch spray (“TS”) ion source; (xv) anano-DESI ion source; (xvi) a laser ablation electrospray (“LAESI”) ionsource; (xvii) a direct analysis in real time (“DART”) ion source;(xviii) a probe electrospray ionisation (“PESI”) ion source; (xix) asolid-probe assisted electrospray ionisation (“SPA-ESI”) ion source;(xx) a cavitron ultrasonic surgical aspirator (“CUSA”) device;(xxi) ahybrid CUSA-diathermy device; (xxii) a focussed or unfocussed ultrasonicablation device; (xxiii) a hybrid focussed or unfocussed ultrasonicablation and diathermy device; (xxiv) a microwave resonance device;(xxv) a pulsed plasma RF dissection device; (xxvi) an argon plasmacoagulation device; (xxvi) a hybrid pulsed plasma RF dissection andargon plasma coagulation device; (xxvii) a hybrid pulsed plasma RFdissection and JeDI device; (xxviii) a surgical water/saline jet device;(xxix) a hybrid electrosurgery and argon plasma coagulation device; and(xxx) a hybrid argon plasma coagulation and water/saline jet device.

Optionally, the step of using said first device to generate aerosol,smoke or vapour comprises contacting said target with one or moreelectrodes.

Optionally, said one or more electrodes comprise either: (i) a monopolardevice, wherein there is optionally provided a separate returnelectrode; (ii) a bipolar device; or (iii) a multi-phase RF device,wherein there is optionally provided at least one separate returnelectrode.

Optionally, said one or more electrodes comprise or forms part of arapid evaporation ionization mass spectrometry (“REIMS”) device.

Optionally, said method further comprises applying an AC or RF voltageto said one or more electrodes in order to generate said aerosol, smokeor vapour.

Optionally, the step of applying said AC or RF voltage to said one ormore electrodes further comprises applying one or more pulses of said ACor RF voltage to said one or more electrodes.

Optionally, said step of applying said AC or RF voltage to said one ormore electrodes causes heat to be dissipated into said target.

Optionally, said step of using said first device to generate aerosol,smoke or vapour from one or more regions of the target further comprisesirradiating the target with a laser.

Optionally, said first device generates aerosol from one or more regionsof the target by direct evaporation or vaporisation of target materialfrom said target by Joule heating or diathermy.

Optionally, said step of using said first device to generate aerosol,smoke or vapour from one or more regions of the target further comprisesdirecting ultrasonic energy into said target.

Optionally, said aerosol comprises uncharged aqueous droplets. Thedroplets may comprise cellular material.

Optionally, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%of the mass or matter generated by said first device and which formssaid aerosol may be in the form of droplets.

The first device may be arranged and adapted to generate aerosol whereinthe Sauter mean diameter (“SMD”, d32) of said aerosol is in a range: (i)<5 μm; (ii) 5-10 μm; (iii) 10-15 μm; (iv) 15-20 μm; (v) 20-25 μm; or(vi) >25 μm.

The aerosol may traverse a flow region with a Reynolds number (Re) inthe range: (i) <2000; (ii) 2000-2500; (iii) 2500-3000; (iv) 3000-3500;(v) 3500-4000; or (vi) >4000.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a Weber number (We) selected from the groupconsisting of: (i) <50; (ii) 50-100; (iii) 100-150; (iv) 150-200; (v)200-250;(vi) 250-300; (vii) 300-350; (viii) 350-400; (ix) 400-450; (x)450-500; (xi) 500-550; (xii) 550-600; (xiii) 600-650; (xiv) 650-700;(xv) 700-750; (xvi) 750-800; (xvii) 800-850; (xviii) 850-900; (xix)900-950; (xx) 950-1000; and (xxi) >1000.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a Stokes number (Sk) in the range: (i) 1-5;(ii) 5-10; (iii) 10-15; (iv) 15-20; (v) 20-25; (vi) 25-30; (vii) 30-35;(viii) 35-40; (ix) 40-45; (x) 45-50; and (xi) >50.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a mean axial velocity selected from the groupconsisting of: (i) <20 m/s; (ii) 20-30 m/s; (iii) 30-40 m/s; (iv) 40-50m/s; (v) 50-60 m/s; (vi) 60-70 m/s; (vii) 70-80 m/s; (viii) 80-90 m/s;(ix) 90-100 m/s; (x) 100-110 m/s; (xi) 110-120 m/s; (xii) 120-130 m/s;(xiii) 130-140 m/s; (xiv) 140-150 m/s; and (xv) >150 m/s.

Optionally, said aerosol comprises uncharged aqueous droplets. Thedroplets may comprise cellular material.

Optionally, the method comprises ionising at least some of said aerosol,smoke or vapour, or analyte therein, so as to generate analyte ions;wherein said analyte ions are analysed to obtain said spectrometricdata.

Optionally, the method comprises directing or aspirating at least someof said aerosol, smoke or vapour into a vacuum chamber of a mass and/orion mobility spectrometer; and/or ionising at least some said aerosol,smoke or vapour, or the analyte therein, within a, or said, vacuumchamber of said spectrometer so as to generate a plurality of analyteions.

Optionally, the method comprises causing said aerosol, smoke or vapour,or analyte therein, to impact upon a collision surface, optionallylocated within a, or the, vacuum chamber of said spectrometer, so as togenerate the plurality of analyte ions.

Optionally, the collision surface may be heated. The collision surfacemay be heated to a temperature selected from the group consisting of:(i) about <100° C.; (ii) about 100-200° C.; (iii) about 200-300° C.;(iv) about 300-400° C.; (v) about 400-500° C.; (vi) about 500-600° C.;(vii) about 600-700° C.; (viii) about 700-800° C.; (ix) about 800-900°C.; (x) about 900-1000° C.; (xi) about 1000-1100° C.; and (xii)about >1100° C.

Optionally, the method comprises adding a matrix to said aerosol, smokeor vapour;

optionally wherein said matrix is selected from the group consisting of:(i) a solvent for said aerosol, smoke or vapour or analyte therein; (ii)an organic solvent; (iii) a volatile compound; (iv) polar molecules; (v)water; (vi) one or more alcohols; (vii) methanol; (viii) ethanol; (ix)isopropanol; (x) acetone; (xi) acetonitrile; (xii) 1-butanol; (xiii)tetrahydrofuran; (xiv) ethyl acetate; (xv) ethylene glycol; (xvi)dimethyl sulfoxide; an aldehyde; (xviii) a ketone; (xiv) non-polarmolecules; (xx) hexane; (xxi) chloroform; (xxii) butanol; and (xxiii)propanol.

Optionally, the method may be carried out using negative ion mode, sooptionally, the method comprises analysing spectrometric data obtainedusing negative ion mode. Optionally, the method may be carried out usingpositive ion mode, so optionally, the method comprises analysingspectrometric data obtained using positive ion mode. Optionally, themethod comprises analysing spectrometric data obtained using negativeion mode and analysing spectrometric data obtained using positive ionmode.

The matrix and/or aerosol, smoke or vapour may be doped with one or moreadditives to, for example, enhance the solvation or dilution of analytewith the matrix, or for enhancing the ionisation of the analyte withinthe aerosol, smoke or vapour.

The doping compound may be an acidic or basic additive such as, forexample, formic acid or diethylamine.

The matrix and/or doping compound may cause derivatisation of theanalyte in the aerosol, smoke or vapour. For example, the matrix and/ordoping compound may cause the derivatisation of cholesterol or steroidsin the analyte. This may render the analyte more easily ionised.

Rapid Evaporative Ionisation Mass Spectrometry (“REIMS”)

Although various different ambient ionization ion sources may be used inthe invention to analyse a variety of targets, a method of REIMSanalysis on a microbial population will now be described in order toassist in understanding the embodiments.

FIG. 10A shows apparatus that may be used to analyse a target, such as amicrobial culture. The apparatus comprises a pair of handheld electrodes106, 108 in the form of a forceps 102 (i.e. the first device); an RFpower supply 103 for supplying an RF voltage to the electrodes 106, 108;an inlet to a mass spectrometer 105; and tubing 104 connecting a port112 at the rear end of the forceps 102 to the inlet of the spectrometer105. The forceps 102 and RF power supply 103 may be configured such thatthe forceps 102 are bipolar forceps. As shown in FIG. 10B, an openentrance port 110 is provided in the tip of one of the electrodes 106 atthe front of the forceps 102. This entrance port 110 opens up into aconduit 111 within the electrode 106. The conduit 111 extends throughthe electrode 106 to an exit port 112 in the rear of the forceps 102, asshown in FIG. 10C.

As shown in FIG. 10A, the sample/target to be analysed may, e.g., beprovided in the form of a microbial pellet 101. The microbial pellet maybe provided in a container 107 such as an Eppendorf tube. The forceps102 may be inserted into contact with the microbial pellet 101 so as toobtain biomass from the microbial pellet 101 on the tips of theelectrodes 106, 108. The two electrodes 106, 108 may be subsequentlybrought into close proximity with each other, e.g., by pinching thebiomass between the tips of the forceps 102. The RF power supply 103 maybe triggered, e.g., using a foot switch, so as to energise theelectrodes 106, 108. This causes the microbial biomass to be rapidlyheated (e.g., by Joule or diathermy heating), due to its non-zeroimpedance, and smoke, aerosol or vapour to be emitted from the biomass.The smoke, aerosol or vapour may contain charged molecular species ofanalytes in the biomass.

Whilst a container 107 such as an Eppendorf tube is shown in FIG. 10A,the target may alternatively be, e.g., microbial biomass such as amicrobial colony, e.g., on a petri dish.

The smoke, aerosol or vapour may then be captured or otherwise aspiratedthrough the entrance port 110 and into the conduit 111 in the forceps102. The smoke, aerosol or vapour is then drawn through the conduit 111,out of the exit port 112, along the tubing 104 and into the inlet of themass spectrometer 105. The inherent vacuum system of the massspectrometer may be used to draw the smoke, aerosol or vapour from theentrance port 110 to the inlet of the spectrometer 105.

FIG. 11 shows a schematic of an embodiment of an interface between thefirst device (e.g., forceps 102) and the mass spectrometer. Theinstrument may comprise an ion analyser 207 having an inlet 206 (whichmay correspond to inlet 5 in FIG. 10A), a vacuum region 208, a collisionsurface 209 and ion optics 212 (such as a Stepwave® ion guide) arrangedwithin the vacuum region 208. The instrument also comprises a sampletransfer tube 202 (corresponding to tubing 4 in FIG. 10 ) and a matrixintroduction conduit 203. The sample transfer tube 202 has an inlet forreceiving the smoke, aerosol or vapour sample 201 (which may correspondto that described in relation to FIG. 10 ) from a sample/target beinginvestigated and an outlet that is connected to the inlet 206 of the ionanalyser 207. The matrix introduction conduit 203 has an inlet forreceiving a matrix compound and an outlet that intersects with thesample transfer tube 202 so as to allow the matrix 204 to be intermixedwith the aerosol sample 201 in the sample transfer tube 202. AT-junction component may be provided at the junction between tubes 202,203 and 206. The tubes 202, 203 and 206 may be removably inserted intothe T-junction.

A method of operating the instrument shown in FIG. 11 will now bedescribed. A sample/target, such as microbial population material, maybe subjected to the REIMS technique. For example, a first device (e.g.,forceps 102) may be used to generate an aerosol, e.g., as describedabove in relation to FIGS. 10A-10C. The aerosol particles 201 are thenintroduced into the inlet of the sample transfer tube 202. A matrixcompound 204 is introduced into the inlet of the matrix introductionconduit 203. The aerosol particles 201 and matrix compound 204 are drawntowards the inlet 206 of the ion analyser 207 by a pressure differentialcaused by the vacuum chamber 208 being at a lower pressure than theinlets to the tubes 202, 203. The aerosol particles 201 may encounterthe molecules of matrix compound 204 in, and downstream of, the regionthat the sample transfer tube 202 intersects with the matrixintroduction conduit 203. The aerosol particles 201 intermix with thematrix 204 so as to form aerosol particles containing matrix molecules205, in which both the molecular constituents of the aerosol sample 201and the matrix compound 204 are present. The matrix molecules 204 may bein excess compared to the molecular constituents of aerosol sample 201.

The particles 205 may exit the sample transfer tube 202 and pass intothe inlet 206 of the ion analyser 207. The particles 205 then enter intothe decreased pressure region 208 and gain substantial linear velocitydue to the adiabatic expansion of gas entering the vacuum region 208from the sample transfer tube 202 and due to the associated free jetformation. The accelerated particles 205 may impact on the collisionsurface 209, where the impact event fragments the particles 205, leadingto the eventual formation of gas phase ions 210 of the molecularconstituents of the aerosol sample 201 and the formation of matrixmolecules 211. The collision surface 209 may be controlled andmaintained at a temperature that is substantially higher than theambient temperature.

The matrix 204 includes a solvent for the analyte 201, such that theanalyte 201 dissolves by the matrix 204, thereby eliminatingintermolecular bonding between the analyte molecules 201. As such, whenthe dissolved analyte 205 is then collided with the collision surface209, the dissolved analyte 205 will fragment into droplets and any givendroplet is likely to contain fewer analyte molecules than it would ifthe matrix were not present. This in turn leads to a more efficientgeneration of analyte ions 210 when the matrix in each droplet isevaporated. The matrix may include a solvent for said aerosol, smoke orvapour or analyte therein; an organic solvent; a volatile compound;polar molecules; water; one or more alcohols; methanol; ethanol;isopropanol; acetone; acetonitrile; 1-butanol; tetrahydrofuran; ethylacetate; ethylene glycol; dimethyl sulfoxide; an aldehyde; a ketone;non-polar molecules; hexane; chloroform; or propanol. Isopropanol is ofparticular interest.

The matrix molecules 211 may freely diffuse into the vacuum. Incontrast, the gas phase ions 210 of the molecular constituents of theaerosol sample 201 may be transferred by the ion optics 212 to ananalysis region (not shown) of the ion analyser 207. The ions 210 may beguided to the analysis region by applying voltages to the ion optics212.

The ion optics 212 may be a StepWave® ion guide. The collision surfacemay be positioned along and adjacent to the central axis of the largeopening of a StepWave® ion guide. As will be understood by those skilledin the art, a StepWave® ion guide comprises two conjoined ion tunnel ionguides. Each ion guide comprises a plurality of ring or other electrodeswherein ions pass through the central aperture provided by the ring orother electrodes. Ions enter a first of the ion guides, along with anyneutrals that may be present, and travel through the first ion guide.Ions are then directed orthogonally into a second of the ion guides andare transmitted therethrough. Transient DC voltages or potentials areapplied to the electrodes to drive the ions through them. The StepWave®ion guide is based on stacked ring ion guide technology and is designedto maximise ion transmission from the source to the mass and/or ionmobility analyser. The device allows for the active removal of neutralcontaminants, since the neutrals are not directed orthogonally into thesecond ion guide, thereby providing an enhancement to overall signal tonoise. The design enables the efficient capture of the diffuse ion cloudentering a first lower stage which is then may focused into an upper ionguide for transfer to the ion analyser. The ions are then analysed bythe ion analyser, which may comprise a mass spectrometer and/or an ionmobility spectrometer, or a combination of the two. As a result of theanalysis, chemical information about the sample 201 may be obtained.

A liquid trap or separator may be provided between the first device(e.g., forceps 2) and the analyser, which captures or discards undesiredliquids that are aspirated by the probe whilst may allowing the smoke,aerosol or vapour itself to pass relatively uninhibited to the massand/or ion mobility spectrometer. This prevents undesired liquid fromreaching the analyser without affecting the measurement of the smoke,aerosol or vapour. The liquid trap or separator may be arranged tocapture the liquid for later disposal.

As described above, although embodiments have been described in whichREIMS is used to generate the smoke, aerosol or vapour for analysis,other ambient ionisation techniques may be used such as, for example,Desorption Electrospray Ionisation (“DESI”).

Desorption Electrospray Ionisation (“DESI”)

Desorption Electrospray Ionisation (“DESI”) has also been found to be aparticularly useful and convenient method for the real time rapid anddirect analysis of microbes and/or compounds. DESI techniques allowdirect and fast analysis of surfaces without the need for prior samplepreparation. The technique will now be described in more detail withreference to FIGS. 12A-12B.

As shown in FIGS. 12A-12B, the DESI technique is an ambient ionisationmethod that involves directing a spray of (primary) electrically chargeddroplets 301 onto a target 304. The electrospray mist is pneumaticallydirected at the target 304 by a sprayer 300 where subsequent splashed(secondary) droplets 305 carry desorbed ionised analytes (e.g., desorbedlipid ions). The sprayer 300 may be supplied with a solvent 306, a gas307 (such as nitrogen) and a voltage from a high voltage source 308.After ionisation, the ions travel through air into an atmosphericpressure interface 309 of a mass and/or ion mobility spectrometer and/ormass and/or ion mobility analyser (not shown), e.g., via a transfercapillary 310. The ions may be analysed by the method described inrelation to FIG. 11 , or by other methods. For example, the transfercapillary 310 of FIG. 12A may correspond to the sample transfer tube 202in FIG. 11 . The transfer capillary 310 may be heated, e.g., to atemperature up to 500° C.

The DESI technique allows, for example, direct analysis of targets, suchas a microbial population, e.g., without requiring any advance samplepreparation for the analysis.

General Methods

Provided is a method of analysis using mass spectrometry and/or ionmobility spectrometry comprising:

(a) using a first device to generate smoke, aerosol or vapour from atarget comprising or consisting of a microbial population;

(b) mass analysing and/or ion mobility analysing said smoke, aerosol orvapour, or ions derived therefrom, in order to obtain spectrometricdata; and

(c) analysing said spectrometric data in order to analyse said microbialpopulation.

Suitable targets are defined elsewhere herein.

The method may optionally be a method of analysing a microbe and/or acompound. Thus, optionally, the method may comprise a step of analysinga microbe and/or compound present in said target on the basis of saidspectrometric data.

It should be understood that any reference herein to “analysing” atarget is intended to mean that the target is analysed on the basis ofthe spectrometric data. Thus, for example, by an expression such as“analysing a microbe” is meant that a microbe is detected, identifiedand/or characterised based upon the spectrometric data. By an expressionsuch as “identifying a microbe” or “analysing spectrometric data inorder to identify a microbe” is meant that the identity of a microbe isdetermined based upon the spectrometric data.

A number of optional features will be described in greater detail below.

The method may optionally be a method of detecting a microbialinfection, such as a vaginal infection; a method of identifying amicrobe, such as a pathogenic microbe, which may optionally include astep of identifying a microbe, such as a pathogenic microbe; a method ofconfirming the presence of a microbe in a sample, which may optionallyinclude a step of confirming the presence of a microbe in a sample; amethod of automatically identifying a microbe, which may optionallyinclude a step of automatically identifying a microbe; a method ofidentifying a cell type in a sample which may optionally include a stepof identifying a cell type in a sample, wherein the cell is optionally amicrobial cell; a method of detecting, identifying and/or characterisinga microbe in a liquid medium, such as a body fluid, which may optionallyinclude a step of detecting, identifying and/or characterising a microbein a liquid medium, such as a body fluid; a method of detecting,identifying and/or characterising a microbe in a food product, which mayoptionally include a step of detecting, identifying and/orcharacterising a microbe in a food product; a method of detecting,identifying and/or characterising a virus, which may optionally includea step of detecting, identifying and/or characterising a virus; a methodof characterising a microbe as being susceptible or resistant to anantimicrobial, such as a bacterium as being susceptible or resistant toan antibiotic, which may optionally include a step of characterising amicrobe as being susceptible or resistant to an antimicrobial, such as abacterium as being susceptible or resistant to an antibiotic; a methodof identifying an infection as being caused by a microbe resistant to orsusceptible to an antimicrobial, such as a bacterium susceptible orresistant to an antibiotic, which may optionally include a step ofidentifying an infection as being caused by a microbe resistant to orsusceptible to an antimicrobial, such as a bacterium susceptible orresistant to an antibiotic; a method of tracking the spread of aninfectious disease, which may optionally include a step of tracking thespread of an infectious disease; a method of detecting, identifying orcharacterising a microbe in a clinical specimen, which may optionallyinclude a step of detecting, identifying or characterising a microbe ina clinical specimen; a method of detecting, identifying orcharacterising a microbe in a microbial mixture, such as a complexmicrobial mixture, which may optionally include a step of detecting,identifying or characterising a microbe in a microbial mixture, such asa complex microbial mixture; a method of detecting, identifying orcharacterising microbial lipids, which may optionally include a step ofdetecting, identifying or characterising microbial lipids; a metabolicprofiling method for identifying a metabolic state of a subjectbiological sample, which may optionally include a step of metabolicprofiling and identifying a metabolic state of a subject biologicalsample; a method for detecting, identifying and/or characterisingproteins and/or metabolites secreted from a microbe, such as anintracellular bacteria, which may optionally include a step ofdetecting, identifying and/or characterising proteins and/or metabolitessecreted from a microbe, such as an intracellular bacteria; and/or amethod of analysing a wound, optionally a method of analysing fluidand/or tissue from a wound optionally comprising using one or moreprotein or other identification databases, which may optionally includea step of analysing a wound, optionally analysing fluid and/or tissuefrom a wound, optionally comprising using one or more protein or otheridentification databases.

The subject of analysis in the method may conveniently be referred to asthe “target entity”. Thus, optionally, the target entity may be amicrobe and/or a compound.

In one aspect, the method may be a method of analysing a microbe, amicrobial interaction, a microbial biomarker, and/or a microbiome. Thus,the method may optionally comprise a step of analysing a microbe, amicrobial interaction, a microbial biomarker, and/or a microbiome.

In one aspect, the method may be a method of analysing the genotypeand/or phenotype of a microbe. Thus, the method may optionally comprisea step of analysing the genotype and/or phenotype of a microbe.

In one aspect, the method may be a method of analysing a faecal and/orbody fluid specimen, e.g., to analyse a microbe and/or compound. Thus,the method may optionally comprise a step of analysing a faecal and/orbody fluid specimen, e.g., to analyse a microbe and/or compound

In one aspect, the method may be a method of analysing a compound. Thus,the method may optionally comprise a step of analysing a compound and/ora biomarker for a compound.

Optionally, the method may include 2 or more of the aspects disclosedherein, e.g., 3 or more, 4 or more 5, or more etc. For example, themethod may optionally comprise a step of analysing a faecal and/or bodyfluid specimen, wherein a microbial biomarker and/or a compoundbiomarker is analysed.

Optional features of any of these methods are discussed below. Thus,unless otherwise stated, any reference to “a method” or “the method” isintended to be a reference to any of the methods provided herein. It isexplicitly intended that any of these features may be present in anycombination in any of these methods.

The method may optionally be a method of screening, e.g., for thepurpose of drug development. Thus, optionally, the method may comprise astep of analysing the response of a microbial population to a test agentor condition.

Optionally, the identity of a microbe may be analysed. Optionally, theinfection of a target may be analysed. Optionally, the homogeneityand/or heterogeneity of a microbial population may be analysed.Optionally, the genotype and/or phenotype of a microbial population orone or more microbial types present therein may be analysed. Optionally,the state of a microbial population or one or more microbial typespresent therein may be analysed. Optionally, a process involving amicrobial population or one or more microbial types present therein maybe analysed. Optionally, the effect of manipulating the genotype and/orphenotype of a microbial population or one or more microbial typespresent therein may be analysed. Optionally, the effect of manipulatingthe environmental conditions of a microbial population may be analysed.Optionally, the method may be used to distinguish between 2 or moredifferent microbial types within a microbial population. Optionally, theeffect of a substance on a microbial population may be analysed.Optionally, the utilisation, production and/or breakdown of a substanceby a microbial population may be analysed. Optionally, a plurality ofmicrobial populations may be analysed to analyse their ability toutilise, produce and/or break down a substance. Thus, optionally, aplurality of microbial populations may be screened to analyse theirproductivity or efficiency with respect to the production, breakdownand/or utilisation of a substance. Optionally, the viability of amicrobial population may be analysed.

The method may be a method of, or of obtaining information relevant to,predicting the viability of a microbial population in terms of its longterm viability, robustness and/or efficiency.

In one aspect, the method may be a method of analysing a disease and/ora biomarker of a disease. Thus, the method may optionally comprise astep of analysing a disease and/or a biomarker of a disease.

The method may be a method of, or of obtaining information relevant to:

(i) diagnosing a disease; (ii) monitoring the progression or developmentof a disease;

(iii) disease prognosis; (iv) predicting the likelihood of a diseaseresponding to treatment; (v) monitoring the response of a disease totreatment; and/or (vi) stratifying subjects.

Thus, the method may optionally comprise a step of analysingspectrometric data and on the basis of that spectrometric data: (i)diagnosing a disease; (ii) monitoring the progression or development ofa disease; (iii) making disease prognosis; (iv) predicting thelikelihood of a disease responding to treatment; (v) monitoring theresponse of a disease to treatment; and/or (vi) stratifying subjects.

Details of suitable diseases are provided elsewhere herein.

In one aspect, the method may be a method of analysing a microbe, amicrobial interaction, and/or a microbial biomarker. Thus, the methodmay optionally comprise a step of analysing a microbe, a microbialinteraction, and/or a microbial biomarker.

In one aspect, the method may be a method of analysing the genotypeand/or phenotype of a cell. Thus, the method may optionally comprise astep of analysing the genotype and/or phenotype of a cell.

In one aspect, the method may be a method of treatment. Thus, the methodmay optionally comprise a step of administering a therapeuticallyeffective amount of a therapeutic agent to a subject in need thereof.

In one aspect, the method may be a method of analysing a compound. Thus,the method may optionally comprise a step of analysing a compound and/ora biomarker for a compound.

Optional features of any of these methods are discussed below. Thus,unless otherwise stated, any reference to “a method” or “the method” isintended to be a reference to any of the methods provided herein. It isexplicitly intended that any of these features may be present in anycombination in any of these methods.

The skilled person will appreciate that any of the methods providedherein may optionally be combined with one or more of the other methodsprovided herein and/or with one or more further methods.

For example, provided is a method which is a combination of two or more,e.g., three or more, four or more or five or more of the methodsdisclosed herein.

Targets and Analysis Thereof

The method may be carried out on a “target”, which may comprise orconsist of a microbial population.

The term “target entity” is used herein to refer to the entity which itis desired to analyse within the target. Thus, any reference to a“target” should be understood to mean a target comprising one or moredifferent target entities. Thus, the target entity may be a microbeand/or compound. For example, the target may be tissue and the targetentity may be bacteria.

The terms “analysis”, “analysing” and derivatives of these terms areused herein to encompass any of the following: detection of a targetentity; identification of a target entity; characterisation of a targetentity; determination of the location of target entity; determination ofa status, e.g., a disease status; and/or determination of the spatialdistribution of target entities.

Any reference to analysing a “microbe” should be understood to mean thata microbial population is analysed, given that single microbes are belowthe detection limit of the method. However, it will be apparent from thecontext that the term “microbe” is sometimes used to indicate that aparticular microbe type within the overall microbial population may beanalysed. Thus, the term “microbe” can be shorthand for a microbialsubpopulation.

The analysis may be qualitative and/or quantitative. Thus, optionally,any type of analysis may involve determining the concentration,percentage, relative abundance or the like of the target entity. Forexample, the relative abundance of microbes in a target, and/or theconcentration of a compound may be analysed. Optionally, an increase ordecrease in a target entity may be analysed.

The terms “detection”, “detecting” and derivations of these terms areused interchangeably herein to mean that the presence or absence of atarget entity or biomarker therefor is determined.

By “identifying” a compound is meant that at least some informationabout the structure and/or function of the compound is obtained, e.g.,the information may optionally allow a compound to be identified ascomprising or consisting of a compound selected from any of the typesdisclosed herein, and/or as being characterised by one or more of thefunctional groups disclosed herein.

The target may optionally consist of a microbial population, e.g., be amicrobial culture. In this context, it should be understood that culturemedium, produced and/or excreted compounds and the like may be present,and that the term “consist of a microbial population” is used toindicate that no non-microbial cellular matter is present. Thus,optionally, the target does not comprise any intact non-microbial cellsand/or no lysed non-microbial cells. Optionally, any reference herein toa target that “consists” of a microbial population is usedinterchangeably with a reference to a target that “consists of amicrobial population and one or more extracellular compounds”.

Alternatively, the target may comprise a microbial population andnon-microbial cellular matter. For example, it may comprise tissue,intact cells and/or lysed cells, e.g., it may be a tissue or tissuespecimen, a faecal specimen, and/or body fluid specimen.

The target may, e.g., be anything on or in which a microbial populationmay be present. Thus, the target may, e.g., a specimen derived from asubject, a clinical specimen, an environmental specimen, a food, abeverage, a plant, a plant specimen, an animal specimen, a subject,and/or an object comprising a microbe.

The microbial population may comprise or consist of one or moredifferent microbial types. Details of microbes are provided elsewhereherein. Optionally, the microbial population may comprise or consist ofmicrobes having the identity of any one of the microbes listed elsewhereherein.

Unless stated otherwise, any reference herein to a “cell” should beunderstood to be a reference to a microbial cell, or to a microbe, inthe case of a unicellular microbe.

Optionally, the microbe population may be mutant and/or transgenic.

Optionally, the microbial population may have, or be/have beengenetically manipulated to have, one or more properties selected fromauxotrophy, production of a desired compound, and/or secretion of adesired compound. Optionally, the microbial population may be, or havebeen, genetically manipulated, e.g., be transgenic and/or have aknock-out genotype and/or phenotype.

Details of genetic manipulation and microbial properties are providedelsewhere herein.

Optionally, the method may comprise the analysis of one or more isogenicmicrobial populations.

Any reference herein to the analysis of a “microbial population” shouldbe understood to mean that the entire microbial population, or a samplethereof, may be analysed.

The method may optionally be carried out on an entire microbialpopulation, or on a sample thereof, or on region of the target,particularly if the target is a subject or a specimen, such as a tissue.

The “subject” may be a human or a non-human animal. The subject may bealive or dead. If the method is carried out on a living subject, then itmay be referred to as an in vivo method. If the method is carried out ona specimen, then it may be referred to as an in vitro or ex vivo method.Thus, the specimen may be from a human or non-human animal.

Optionally, the animal may be a mammal, optionally selected, forexample, from any livestock, domestic or laboratory animal, such as,mice, guinea pigs, hamsters, rats, goats, pigs, cats, dogs, sheep,rabbits, cows, horses and/or monkeys. Optionally, it may be an insect,bird or fish, e.g., a fly or a worm. Thus, veterinary applications arecontemplated.

The method may optionally be carried out on an in vivo target, i.e. on aliving subject. For example, it may be carried out by using a thermalablation method.

Alternatively or in addition, it may optionally be carried out on a deadsubject, for example as part of an autopsy or a necropathy.

Alternatively or in addition, it may optionally be carried out on an exvivo or in vitro target, e.g., on a specimen, which may be a clinicalspecimen. The specimen may optionally be a provided specimen, i.e. aspecimen that was previously obtained or removed from a subject.Optionally, the method may include a step of obtaining a specimen from asubject.

Thus, it may optionally be carried out on a specimen, which mayoptionally be selected, for example, from a tissue specimen, a bodyfluid specimen and/or a faecal specimen. For example, it may be asurgical resection specimen, a biopsy specimen, a swab, and/or a smear.

A tissue specimen may comprise or consist of human tissue, non-humananimal tissue, and/or plant tissue.

Resection is the surgical removal of part or all of a tissue.

A biopsy specimen may optionally be obtained, e.g., by using a needle towithdraw tissue and/or fluid comprising cells; by using an endoscope;and/or during surgery. A biopsy may optionally be incisional,excisional, or be retrieved from a surgical resection. A biopsy specimencomprises cells and may optionally be a tissue specimen, for example,comprising or consisting of diseased and/or non-diseased tissue.

A “swab” is intended to be understood as comprising a “standard medicalswab” i.e. a swab that is designed for sampling biological samples suchas mucosal membranes. For example, the term “standard medical swab”should be understood as covering a “cotton bud” (British) or a “cottonswab” (American) i.e. a small wad of cotton wrapped around one or bothends of a tube. The tube may be made from plastic, rolled paper or wood.

A swab may optionally, for example, comprise a tissue or other cellularmaterial, e.g., a mucosal sample.

A smear may, for example, optionally be a specimen that has been smearedonto a solid support, e.g., between two slides.

A body fluid may, for example, optionally be selected from blood,plasma, serum, sputum, lavage fluid, pus, urine, saliva, phlegm, vomit,faeces, amniotic fluid, cerebrospinal fluid, pleural fluid, semen,vaginal secretion, interstitial fluid, and/or lymph. Optionally, it maybe dried, collected with a swab, and/or dispensed onto an absorbentcarrier, e.g., a filter or paper. Optionally, it may be a pellet. Apellet may be prepared, e.g., as described below.

The analysis of a target tissue, faecal and/or body fluid specimen mayprovide information about a microbe, microbe-associated disease and/ormicrobiome, optionally a mucosal microbiome and/or the microbiome of theGI lumen. Thus, optionally, the method may involve the analysis of afaecal and/or body fluid specimen. For example, a faecal and/or bodyfluid specimen may be analysed for the presence of a compound, and/or amicrobe.

The method may optionally allow an analysis of metabolic differencesbetween various microbial populations. By identifying taxonomic specificbiomarkers the method may optionally allow the analysis, e.g.,diagnosis, of microbial infections and/or mixed microbial communities.

Optionally, a faecal and/or body fluid specimen may be analysed for thepresence of a microbe, to analyse a microbial population, to analyse acompound and/or to analyse a microbiome.

By “microbial culture” is meant a microbial population that was culturedto maintain and/or propagate the microbe. A microbial culture may, e.g.,be a liquid, semi-solid or solid culture. A microbial culture may, e.g.,be a culture from a culture collection, a culture derived from anenvironmental sample, a culture derived from a clinical specimen, aculture derived from a subject or object, and the like. A culture may be“derived” from a subject, specimen and the like by taking a sample,e.g., via a swab, inoculating said sample in on or a suitable culturemedium, and incubating the culture medium under suitable conditions fora suitable length of time to allow any microbes present therein to grow.Details of suitable culture media, which may be solid, soft or liquid,and culture conditions, are well known and also provided elsewhereherein.

The microbial culture may, e.g., be in the form of a suspension culture,a colony, a bacterial or fungal lawn, a viral plaque, or a biofilm.Optionally, it may be dried, collected with a swab, and/or dispensedonto an absorbent carrier, e.g., a filter or paper. Optionally, it maybe a pellet. A pellet may be prepared, e.g., as described below.

An object comprising a microbial population may be any object, which mayoptionally be known or suspected to comprise a microbe, e.g., to beinfected, contaminated, or colonised, by a microbe. The object may,e.g., be a household object, a tool, a surgical tool or instrument, apiece of furniture, a worktop, an item of clothing, a fabric, or thelike.

A food may, e.g., be meat, fish, vegetable, fruit, and/or nut. It mayoptionally be a processed food, such as a sausage or pasty, or anunprocessed food, such as a steak.

The method may optionally involve the analysis of one or more differenttargets. Optionally, 2 or more targets from different subjects orobjects, and/or from different locations within a subject or object, maybe analysed. Optionally, the targets may be at or from 2 or moredifferent locations, e.g., specimens may be at or from 2 or morelocations in/of a subject or object.

Optionally, a target may be at or from one or more locations known orsuspected to be healthy, non-infected, or sterile; and one or morelocations known or suspected to be diseased, infected, or non-sterile.

Optionally, the method may involve the analysis of 2 or more locationsof a target. Optionally, distinct locations of a target may be analysed,e.g., a series of points may be sampled, optionally with or withoutspatial encoding information for imaging purposes.

The analysis may optionally be made intra-operatively, i.e. whilst asurgical procedure is under way. Thus, the analysis may optionally beused to provide real-time analysis of a target. The analysis mayoptionally be used to identify disease margins. A disease margin mayoptionally be analysed, e.g., by analysing the concentration of aparticular microbe type in a target region. The analysis may optionallybe made in vivo, e.g., during a surgical procedure. This may optionallyinvolve using, e.g., a thermal ablation surgical method, e.g., REIMStechnology, such as the iKnife technology. For example, a tissue onwhich surgery is being performed, e.g., an infected tissue, may beanalysed in vivo and the results of the analysis may be used to inform,influence or determine a further surgical step.

The surgery may optionally be surgery in relation to any of the diseasesmentioned herein, such as cancer surgery and the like. The surgery mayoptionally be laparoscopic, and/or endoscopic.

The analysis may optionally be made in vitro or ex vivo. This mayoptionally be, e.g., in parallel to a surgical procedure. For example, aspecimen, such as, a biopsy, may be obtained during a surgicalprocedure. Such a provided specimen may then be analysed ex vivo and theresults of the analysis may be used to inform, influence or determine afurther surgical step.

The method may optionally be carried out on a target that is native. By“native” is meant that the target has not been modified prior toperforming the method provided herein. In particular, the target may benative in that the microbes are not subjected to a step of lysis orextraction, e.g., lipid extraction, prior to performance of the methodprovided herein. Thus, a target may be native in that all orsubstantially all of the microbes in the microbial population areintact. Thus, a target may be native in that it has not been chemicallyor physically modified and is thus chemically and physically native.Optionally, the target may be chemically native, i.e. it may bechemically unmodified, meaning that it has not been contacted with achemical agent so as to change its chemistry. Contacting a target with amatrix is an example of a chemical modification.

Optionally, the target may be physically native, i.e. it may bephysically unmodified, meaning that it has not been modified physically.Freezing, thawing, and/or sectioning are examples of physicalmodifications. The skilled person will appreciate that although physicalactions, such as, freezing, may affect a specimen's chemistry, for thepurpose of the methods provided herein such an action is not consideredto be a chemical modification.

Thus, optionally the target may be chemically native, but not physicallynative, e.g., because it has been frozen and/or sectioned.

Optionally, the target may be frozen, previously frozen and then thawed,fixed, sectioned, and/or otherwise prepared, as discussed with regard tospecimen preparation. Optionally, the method may be carried out on atarget that has not undergone a step of preparation specifically for thepurpose of mass spectrometry and/or ion mobility analysis.

The target may optionally not be/have been contacted with a solvent, ora solvent other than water, prior to generating the smoke, aerosol orvapour from the target.

Additionally, or alternatively, the target may optionally not be/havebeen contacted with a matrix prior to generating the smoke, aerosol orvapour from the target. For example, the target may not be/not have beencontacted with a MALDI matrix or other matrix for assisting ionisationof material in the target. A MALDI matrix may, e.g., comprise or consistof small organic acids such as α-cyano-4-hydroxycinnamic acid (CHCA)and/or 2,5-dihydroxybenzoic acid (DHB).

The method may optionally be carried out on a target that has beenprepared for a particular mass spectrometry and/or ion mobilityanalysis; and/or that has been prepared for any of the analyticalmethods mentioned elsewhere herein.

Specimen preparation (for any of the methods provided herein and/or anyof the analytical methods disclosed herein) may optionally involve oneor more of the following.

The specimen or part thereof may optionally be deposited on a solidsurface, such as, a glass or plastic slide.

The specimen may optionally be fixed chemically, or via a frozen sectionprocedure, e.g., to preserve tissue and/or microbes from degradation,and to maintain the structure of the cells and of sub-cellularcomponents such as cell organelles, e.g., nucleus, endoplasmicreticulum, and/or mitochondria. The fixative may, for example, be 10%neutral buffered formalin. The specimen may optionally be processed withe.g., epoxy resins or acrylic resins to allow or facilitate sections tobe cut. The sample may optionally be embedded, for example, in paraffin.The specimen may optionally be cut into sections of, for example, 1 μmto 200 nm. For example, the specimen may optionally be about 5 μm thickfor light microscopy, or about 80-100 nm thick for electron microscopy.Optionally, the specimen may be cut into sections of at least 1, 3, 5,7, 9, 10, 12, 14, 16, 18, 20, 22, 24 or 25 μm and no more than 100, 90,80, 70, 60, 50, 40, 35, 30, 28, or 26 μm, for example, 5-25 μm.

Frozen sections may optionally be prepared, e.g., by freezing andslicing the specimen. Prior to freezing, the specimen may optionally beembedded, e.g., as described above. Embedding medium helps conduct heataway from the specimen during freezing, helps protect the tissue fromdrying during storage, and supports the tissue during sectioning.

Freezing may optionally be performed, e.g., by contacting the specimenwith a suitable cooling medium, such as, dry ice, liquid nitrogen, or anagent that has been cooled in dry ice or liquid nitrogen, e.g.,isopentane (2-methyl butane). Frozen specimens may optionally be storedat, e.g., between about −80 and −4 degrees Celsius, e.g., at −70 or −20degrees Celcius and may optionally be thawed prior to performance of themethod described herein.

The specimen or sections thereof may be stained, for example, withHematoxylin and eosin (H&E stain). Hematoxylin, a basic dye, stainsnuclei blue due to an affinity to nucleic acids in the cell nucleus;eosin, an acidic dye, stains the cytoplasm pink.

Optionally, a lesion, optionally of a tissue, may be analysed. A lesionis region in a tissue which is abnormal as a consequence of, e.g.,injury or disease. The lesion may, for example, be selected from awound, an ulcer, an abscess, and/or a tumour. The lesion may, forexample, be a diabetic lesion, such as, a diabetic limb or digit, or adiabetic ulcer.

Optionally, tissue may be analysed, e.g., tissue affected by, or in thevicinity of, cancer or necrosis.

The tissue may optionally be selected from adrenal gland tissue,appendix tissue, bladder tissue, bone, bowel tissue, brain tissue,breast tissue, bronchi, ear tissue, oesophagus tissue, eye tissue,endometrioid tissue, gall bladder tissue, genital tissue, heart tissue,hypothalamus tissue, kidney tissue, large intestine tissue, intestinaltissue, larynx tissue, liver tissue, lung tissue, lymph nodes, mouthtissue, nose tissue, pancreatic tissue, parathyroid gland tissue,pituitary gland tissue, prostate tissue, rectal tissue, salivary glandtissue, skeletal muscle tissue, skin tissue, small intestine tissue,spinal cord, spleen tissue, stomach tissue, thymus gland tissue, tracheatissue, thyroid tissue, ureter tissue and/or urethra tissue, soft andconnective tissue, peritoneal tissue, blood vessel tissue and/or fattissue; grade I, grade II, grade III or grade IV cancerous tissue;metastatic cancerous tissue; mixed grade cancerous tissue; a sub-gradecancerous tissue; healthy or normal tissue; or cancerous or abnormaltissue.

Optionally, the method may involve the analysis of a mucosal target,which may be in vivo, or a specimen comprising or consisting of mucosa.Optionally, the method may involve the analysis of a mucosal target toanalyse a mucosal microbe; to analyse a microbial interaction with themucosa, and/or to analyse the mucosal microbiome.

The mucosa lines several passages and cavities of the body, particularlythose with openings exposed to the external environment, including theoral-pharyngeal cavity, gastrointestinal (GI) tract, respiratory tract,urogenital tract, and exocrine glands.

Thus, the mucosa may optionally be selected from Bronchial mucosa,Endometrium (mucosa of the uterus), Esophageal mucosa, Gastric mucosa,Intestinal mucosa (gut mucosa), Nasal mucosa, Olfactory mucosa, Oralmucosa, Penile mucosa and/or Vaginal mucosa.

Sampling

The term “sampling” is used herein to refer to the use of a device togenerate smoke, vapour or aerosol from a target.

Any of the methods may optionally include automatic sampling, which mayoptionally be carried out using, e.g., a REIMS device. Any of themethods may optionally comprise using a disposable sampling tip.

Optionally, the target may comprise or consist of a microbial culturecultured on a solid culture medium. The microbial culture may optionallybe sampled directly from said culture medium, or it may optionally besampled from a support onto which it has been transferred. For example,prior to sampling, a microbial culture may be transferred onto a hardsupport made, e.g., of glass or plastic, such as a microscope slide.

Optionally, the target may comprise or consist of a liquid, which mayoptionally be a microbial culture cultured in a liquid culture medium, abody fluid or clinical fluid specimen, an environmental sample, a food,or a beverage.

The liquid may optionally be processed prior to sampling, for example bycentrifugation or filtration. Optionally, centrifugation may be carriedout at a force and for a time suitable to deposit any microbes. Themicrobes form a pellet and the supernatant may be removed.

The force may optionally be selected from at least 1,000×g, 2,000×g,3,000×g, 4,000×g, 5,000×g, 6,000×g, 7,000×g, 8,000×g, 9,000×g, 10,000×g,11,000×g, or at least 12,000×g and optionally less than 15,000×g,14,000×g, 13,000×g, 12,000×g, 11,000×g, 10,000×g, 9,000×g, 8,000×g,7,000×g, 6,000×g, or 5,000×g. The centrifugation time may optionally beat least 5, 6, 7, 8, 9, 10, 12, 15, 18, 20, 25, 30, 40 or 50 minutes andoptionally less than 60, 50, 40, 30 or 25 minutes. For many microbes, aforce of about 5000 g for about 10-20 minutes may typically be used. Theskilled person will be able to determine what rotation per minute (rpm)must be selected based on the size of the centrifuge tube to achieve thedesired gravitational force g.

The pellet may be sampled straight from the bottom of the centrifugetube, or it may be sampled from a support onto which it has beentransferred. For example, prior to sampling, a pellet may optionally betransferred onto a glass or plastic support, such as a slide, or onto aswab, such as, a cotton swab.

For example, if the microbial pellet contains only small amounts ofbiomass or excess liquid, the microbial biomass may optionally betransferred to a solid support material such as a common cotton swab. Toachieve this, some of the microbial biomass may be picked up from thecell pellet after centrifugation using a swab. Subsequently, parts ofthe swab may be squeezed between the electrodes of the sampling probewhile simultaneously sampling using electrical current. The inventorshave determined that such a transfer step does not significantly affectthe resulting spectral profiles.

Alternatively, the liquid may be processed by filtration to deposit anymicrobes on a filter or mesh, which may then be sampled. Thus, themethod may further comprise aspirating or passing the liquid through afilter media.

The method may further comprise analysing residue on the filter mediawhich remains after the liquid has been aspirated or passed through thefilter media.

Thus, the analysis of a liquid may optionally include, or be precededby, a processing step to remove excess liquid; thereby providing atarget that is devoid of excess liquid. Such a target which was obtainedfrom a liquid but from which excess liquid has been removed may bereferred to herein as a “solid” target. It will be understood that theterm “solid” is used in biology to refer to samples that appear solid tothe naked eye, e.g., form a lump, even if they do not have the physicalcharacteristics of a solid. Thus, a “solid” target includes, forexample, microbial colonies, microbial cell pellets, a microbialbiofilm, smears of any thereof, or animal tissue.

The analysis of a target may optionally involve the use forceps-basedREIMS technology, wherein a sample of the target may be taken betweenthe forceps and the probes may then be drawn together.

The microbial population, e.g., a pellet, may optionally be subjected toone or more washing steps, e.g., to remove the culture medium. Washingmay be performed with a suitable buffer. Thus, the method may optionallybe performed on a washed microbial population.

Biomarkers

The method may optionally involve the analysis of one or morebiomarkers. A biomarker may be an objective, quantifiable characteristicof, e.g., the presence of a microbe, a microbe type, microbecharacteristic, compound, and/or biological process.

The term “biomarker” is sometimes used explicitly herein, but it shouldalso be understood that any of the analyses mentioned herein mayoptionally be the analysis of a biomarker. Thus, e.g., any reference toanalysing a “microbe” should be understood optionally to be “analysing amicrobial biomarker”; any reference to analysing a “compound” should beunderstood optionally to be “analysing a biomarker for that compound”;and so on.

The biomarker may optionally be a spectral biomarker. The term “spectralbiomarker” is used interchangeably herein with “spectrometric biomarker”and is used herein to refer to spectrometric data that is characteristicof a cell type, disease status, microbe, compound, and/or biologicalprocess, but for simplicity, a spectral biomarker may simply be referredto as a “biomarker”.

By “characteristic of a microbe type” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said microbe type. Optionally, the biomarker may be used todistinguish between microbes of different taxa, e.g., kingdoms, phyla,classes, genera, species and/or strains; between genotypically and/orphenotypically different microbes; between an animal cell and amicrobial cell; between a normal and an abnormal microbe; and/or betweena wild-type and a mutant microbe.

By “characteristic of a microbe” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said microbe. As discussed elsewhere herein, identificationmay be on any level, for example, on a taxonomic level. A biomarker thatallows identification of a microbe as belonging to a particulartaxonomic level may be referred to as a “taxonomic marker” or “taxonomicbiomarker”. Thus, a taxonomic marker may be specific for a Kingdom,Phylum, Class, Order, Family, Genus, Species and/or Strain.

By “characteristic of a compound” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said compound.

By “characteristic of a biological process” is meant that the biomarkermay optionally be used to analyse a biological process. Optionally, thebiomarker may be used to analyse the start, progression, speed,efficiency, specificity and/or end of a biological process.

Different microbe types, compounds, biological progresses and the likemay be characterised by the presence or absence, and/or relativeabundance, of one or more compounds, which may serve as biomarkers. Anyreference herein to a biomarker being a particular compound, or class ofcompounds, should be understood optionally to be the spectrometric dataof that compound, or class of compounds.

For example, a reference to a “C24:1 sulfatide (C48H91NO11S)” biomarkershould be understood to be a reference to the spectrometric datacorresponding to C24:1 sulfatide (C48H91NO11S) which may, e.g., be asignal at m/z of about 888.6; whereas a reference to a “glycosylatedceramide” biomarker should be understood to be a reference to thespectrometric data corresponding to glycosylated ceramide, which may,e.g., be a signal at m/z of 842, 844 or 846.

As explained above, a biomarker may be indicative of a cell type,disease status, microbe, compound, and/or biological process. Abiomarker which is indicative of Pseudomonas aeruginosa may be referredto as a “Pseudomonas aeruginosa biomarker” and so on.

Optionally, a spectral biomarker may be identified as being thespectrometric data of a particular compound, or class of compounds.Thus, a signal corresponding to a particular mass, charge state, m/zand/or ion mobility may optionally be identified as being indicative ofthe presence of a particular compound, or class of compounds.

Optionally, a spectral signal may serve as a biomarker even if adetermination has not been made as to which particular compound, orclass of compounds gave rise to that signal. Optionally, a pattern ofspectral signals may serve as a biomarker even if a determination hasnot been made as to which particular compounds, or class of compounds,gave rise to one or more signals in that pattern, or any of the signalsin a pattern.

The work disclosed herein has led to the identification of a range ofbiomarkers, as well as allowing the identification of furtherbiomarkers. Optionally, the biomarker may be selected from any of thebiomarkers disclosed herein, including in any of the Examples and/orTables. Optionally, the biomarker may be a biomarker of the substitutedor unsubstituted form of any of the biomarkers mentioned herein; and orof an ether, ester, phosphorylated and/or glycosylated form, or otherderivative, of any of the biomarkers mentioned herein.

Optionally, the biomarker may be a biomarker of a lipid; a protein; acarbohydrate; a DNA molecule; an RNA molecule; a polypeptide, such as, aribosomal peptide or a non-ribosomal peptide; an oligopeptide; alipoprotein; a lipopeptide; an amino acid; and/or a chemical compound,optionally an organic chemical molecule or an inorganic chemicalmolecule.

A biomarker may optionally be the clear-cut presence or absence of aparticular compound, which may optionally manifest itself as thepresence or absence of a spectrometric signal corresponding to aspecific mass, charge state, m/z and/or ion mobility(e.g. collisioncross section).

A biomarker may optionally be the relative abundance of a particularbiomolecule or compound, which may optionally manifest itself as therelative intensity of a spectrometric signal corresponding to a specificmass, charge state, m/z and/or ion mobility.

A biomarker may optionally be the relative abundance of more or morecompounds, which may optionally manifest itself as the relativeintensity of two or more spectrometric signals corresponding to two ormore mass, charge state, m/z and/or ion mobility.

Thus, a biomarker may optionally be an increased or decreased level ofone or more compounds, e.g., a metabolite, a lipopeptide and/or lipidspecies, which may optionally manifest itself as an increase and/ordecrease in the intensity of two or more spectrometric signalscorresponding to two or more mass, charge state, m/z and/or ionmobility.

The presence, absence and relative abundance of a variety of compoundsmay be referred to as a molecular “fingerprint” or “profile”. Thetotality of the lipids of a cell may be referred to as a lipidomicfingerprint/profile, whereas the totality of metabolites produced by acell may be referred to as a metabolomic fingerprint/profile.

Thus, the biomarker may be a molecular fingerprint, e.g., a lipidfingerprint and/or a metabolomic fingerprint, more particularly e.g., a(i) a lipidomic profile; (ii) a fatty acid profile; (iii) a phospholipidprofile; (iv) a phosphatidic acid (PA) profile; (v) aphosphatidylethanolamine (PE) profile; (vi) a phosphatidylglycerol (PG)profile; (vii) a phosphatidylserines (PS) profile; or (viii) aphosphatidylinositol (PI) profile.

By way of example, phosphatidylglycerol may be found in almost allbacterial types, but it may be present in different bacteria indifferent relative amounts. Phosphatidylglycerol may be present at alevel of only 1-2% in most animal tissues. It may therefore be abiomarker for bacteria in an animal specimen, and/or be a biomarker forspecific types of bacteria.

The biomarker may optionally be a direct biomarker or an indirectbiomarker. By “direct” biomarker is meant that the spectrometric data isproduced directly from the biomarker. For example, if a particularcompound has a specific spectrometric signal or signal pattern, thenobtaining this signal or signal pattern from a sample provides directinformation about the presence of that compound. This may be the case,for example, for a metabolite produced in significant amounts by a cellor microbe. Optionally, in such an example, the spectrometric data fromthe compound may alternatively or in addition serve as an indirectbiomarker for the cell or microbe that produced this compound.

By “indirect” biomarker is meant that the spectrometric data is producedfrom one or more biomarkers that is/are indicative of a particularcompound, biological process, and/or type of microbe or cell. Thus, anindirect biomarker is spectrometric data generated from one or moremolecules that provides information about a different molecule. Forexample, a molecular fingerprint, such as, a lipid fingerprint, may beindicative of the expression of a particular protein, e.g., a receptor;or of a particular cell type or microbial type.

A lipid biomarker may optionally be selected from, e.g., fatty acids,glycerolipids, sterol lipids, sphingolipids, prenol lipids,saccharolipids and/or phospholipids. A brief overview of various lipidsis provided below, but it must be appreciated that any particular lipidmay fall into more than one of the groups mentioned herein.

A fatty acid is an aliphatic monocarboxylic acid. The fatty acid mayoptionally have a carbon chain comprising precisely or at least 4, 6, 8,10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 36, 38 or 40 carbons. Itmay optionally be monounsaturated, polyunsaturated, or saturated. It mayoptionally be an eicosanoid. It may, for example, be oleic acid,palmitic acid, arachidonic acid, a prostaglandin, a prostacyclin, athromboxane, a leukotriene, or an epoxyeicosatrienoic acid.

The glycerolipid may optionally be selected from e.g., monoacylglycerol,diacylglycerol, and/or triacylglycerol.

The sterol may optionally be selected from free sterols, acylatedsterols (sterol esters), alkylated sterols (steryl alkyl ethers),sulfated sterols (sterol sulfate), sterols linked to a glycoside moiety(steryl glycosides) and/or acylated sterols linked to a glycoside moiety(acylated sterol glycosides).

The sterol may optionally have an aliphatic side chain of precisely orat least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 21, 22, 23,24, 25, 26, 27, 28, 29, 20, 35 or 40 carbon atoms. The number of carbonatoms in the aliphatic side chain may be expressed by the letter Cfollowed by the number, e.g., C27 for cholesterol. It may, for example,be selected from cholesterol, cholesterol sulphate, ergosterol,lanosterol, dinosterol (4α,23,24-trimethyl-5α-cholest-22E-en-3β-ol),oxysterol and/or a derivative of any thereof.

A phospholipid may comprise two fatty acids, a glycerol unit, aphosphate group and a polar molecule. The Phospholipid may optionallycomprise an ester, ether and/or other O-derivative of glycerol. Thephospholipid may optionally be selected from, e.g.,Phosphatidylglycerol, diphosphatidylglycerol (cardiolipin),Acylphosphatidylglycerol(1,2-diacyl-sn-glycero-3-phospho-(3′-acyl)-1′-sn-glycerol), and/orplasmalogen.

The phosphatidylglycerol lipid may optionally be selected fromphosphatidic acids (PAs), phosphatidylethanolamines (PEs),phosphatidylglycerols (PGs), phosphatidylcholines (PCs),phosphatidylinositols (PIs) and/or phosphatidylserines (PSs).

A sphingolipid is a lipid containing a sphingoid. It may optionally beselected from, e.g., a ceramide, i.e. an N-acylated sphingoid;sphingomyelin, i.e. a ceramide-1-phosphocholine; phosphoethanolaminedihidroceramide, and/or a glycosphingolipid, i.e. a lipid containing asphingoid and one or more sugars. For example, it may optionally be aglycosylated ceramide.

The biomarker may optionally be a metabolite, such as, a primary or asecondary metabolite; an antibiotic; a quorum sensing molecule; a fattyacid synthase product; a pheromone; and/or a biopolymer.

A biomarker compound may optionally be characterised by one or more ofthe following functional groups: alcohol, ester, alkane, alkene, alkyne,ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic,carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it mayadditionally be identified as being primary, secondary or tertiary,e.g., a primary alcohol, a secondary amine, or the like.

For example, it may optionally be a terpene; prenylquinone; sterol;terpenoid; alkaloid; glycoside; surfactin; lichenysin,2-Heptyl-3-hydroxy-4(1H)-quinolone or 2-heptyl-3,4-dihydroxyquinoline(“PQS” or Pseudomonas quinolone signal); 4-hydroxy-2-heptylquinoline(“HHQ”); phenol, such as, a natural phenol; phenazine; biphenyl;dibenzofurans; beta-lactam; polyketide; rhamnolipid; mycolic acids;and/or polyhydroxyalkanoates;

The biomarker may optionally be selected from, e.g.,Glycerophosphocholines, Sphingomyelins, Glycerophospholipids,Galactoceramides, Glycerophosphoinositols, Glycerophosphoserines,Glycerophosphoglycerols, Cholesterol sulphate, sulfatides, seminolipids,citric acid, Glycerophosphoethanolamines, Glycerophosphoethanolamines,2-hydroxygluterate, glutamine, glutamate, succinate, fumarate,palmitoylglycine, ubiquinones, gadoteridol and/or any of the otherbiomarkers mentioned herein, including any of the Tables.

The inventors have identified inter alia the biomarkers listed in any ofthe Tables, as well as the following biomarkers:

Mycolic acids for bacteria belonging to the Corynebacterineae subordersuch as Mycobacterium spp., Corynebacterium spp. and Rhodococcus spp. Inparticular, the following mycolic acids have been detected from thecorresponding genera:

Mycobacterium spp.: C77-C81 (even and odd numbered, 0-2 unsaturations);

-   Corynebacterium spp.: C28-C36 (even numbered, 0-2 unsaturations);-   Nocardia spp.: C48-C56 (even numbered, 0-3 unsaturations);-   Rhodococcus spp.: C28-C38 (even and odd numbered, 0-4    unsaturations).

A variety of sphingolipid species were found to be specific for membersof the Bacteroidetes phylum. These sphingolipids include oxidizedceramides species, phosphoethanolamine dihydroceramides andC15:0-substituted phosphoglycerol dihydroceramides and dihydroceramide.Among those sphingolipid species, a series of galactosylatedsphingolipids was found to be specific for Bacteroides fragilis(Bacteroides fragilis alpha-Galactosylceramides).

Among bacteria, plasmalogens are highly specific for anaerobic bacteriasuch as Clostridium spp. and Fusobacterium spp. This is due to the factthat aerobic bacteria lost the biochemical pathway required forplasmalogen synthesis. Humans are able to synthesize plasmalogens(although via a different biochemical pathway from anaerobes), althoughthese were generally found to have longer chain lengths than bacterialplasmalogens.

Other biomarkers that are indicative of a certain group of bacteriainclude, for instance, lipopeptides that are produced specifically bycertain Bacillus species, such as, surfactin for B. subtilis andlichenysin for B. licheniformis. Production of these two molecules alsoenables straightforward differentiation of these otherwise very closelyrelated bacteria. A further example includes PQS-derived quorum-sensingmolecules and mono- and di-rhamnolipid species found for Pseudomonasaeruginosa.

Quorum sensing is a form of cell-to-cell communication which relies onthe principle that when a single microbe releases quorum sensingmolecules into the environment, the concentration of such molecules istoo low to be detected. However, when sufficient bacteria are present,quorum sensing molecule concentrations reach a threshold level thatallows the microbes to sense a critical cell mass and, in response, toactivate or repress particular genes. Quorum sensing molecules maytherefore also be referred to as autoinducers. Pathogens may use quorumsensing molecules as virulence factors.

Some examples of quorum sensing molecules are listed above. Additionalexamples include N-acyl homoserine lactones (N-acyle HSLs), such as,3-oxo-C₈-HSL, 3-oxo-C₁₀-HSL, or 3-oxo-C₁₂-HSL; diketopiperazines;3-hydroxypalmitic acid methyl ester; and peptide-based quorum sensingmolecules, such as, that of Staphylococcus aureus, which is anoligopeptide that has been termed the autoinducing peptide (AIP),encoded by the gene agrD. The active AIP is 7-9 amino acids, with a5-membered thiolactone ring.

By way of example, sphingomyelin lipids may optionally be a biomarker,e.g., for cancer; ergosterol may optionally be a biomarker, e.g., forfungi; dinosterol may optionally be a biomarker, e.g., fordinoflagellates; cholesterol sulphate may optionally be a biomarker,e.g., for cancer; 2-hydroxygluterate may optionally be a biomarker,e.g., for cancer; and/or one or more sulfatides may optionally be abiomarker, e.g., for cancer, for example, astrocytoma. Optionally, thesulfatide may be selected from C₄₈H₉₁NO₁₁S, C₄₈H₉₂NO₁₂S, and/orC₅₀H₉₄NO₁₁S.

Iso-C15:0-substituted phosphoglycerol dihydroceramides may be specificfor the Porphyromonadaceae family. m/z=566.4790 may be a biomarker formembers of the Flavobacteria class.

The method provided herein may optionally involve the analysis of anexogenous compound, i.e. a compound that was administered to a subject,brought into contact with a subject or specimen, or to which a microbewas exposed. Thus, the biomarker may be an exogenous compound.

Analysis of Spectrometric Data

Any of the methods provided herein may optionally involve the analysisof spectrometric data; more particularly, the analysis of spectrometricdata from a first sample. In particular, the detection, identificationand/or characterisation of a microbe and/or a compound may involve theanalysis of spectrometric data (from a first sample). The detection,identification and/or characterisation of a microbe and/or a compoundmay be based solely on the analysis of spectrometric data, or it mayoptionally involve one or more further analytical tools, details ofwhich are discussed elsewhere herein.

In some embodiments, the spectrometric data provides direct informationabout the target. For example, if a particular microbe has a specificspectrometric pattern, then obtaining this pattern from a sampleprovides direct information about the presence of that microbe. Foranother example, if a particular compound has a specific spectrometricpattern, then obtaining this pattern from a sample provides directinformation about the presence of that compound. This may be the case,for example, for a compound which is secreted by a microbe.

However, in other embodiments, spectrometric data provides indirectinformation about the target. This may be the case, for example, for acompound which is produced but not secreted by a microbe. The presenceof this compound may be detected indirectly by detecting a spectrometricpattern which is characteristic of a microbe containing said compound.

Spectrometric data obtained using a sample, e.g., a first sample, mayoptionally be compared to one or more other spectrometric data, whichmay conveniently be referred to herein as “reference”, “control” or“comparator” spectrometric data.

The term “reference” spectrometric data is used herein to meanspectrometric data from a known microbe or compound. Referencespectrometric data may optionally be publicly available, or the skilledperson may generate a library of reference spectrometric data. Any ofthe methods provided herein may optionally involve comparing thespectrometric data to one or more reference spectrometric data. If thespectrometric data obtained from a sample matches or correspondssufficiently to a reference spectrometric data, then optionally apositive determination may be made. If the spectrometric data obtainedfrom a sample does not match or correspond sufficiently to a referencespectrometric data, then optionally a negative determination may bemade. Optionally, a positive determination may be made if thespectrometric data corresponds more closely to one library entry thanany other library entry.

The term “comparator” spectrometric data is used herein to meanspectrometric data obtained from a second sample. The first and secondsample may be obtained from different samples, or from the samedifferent locations of the same sample. Any of the methods providedherein may optionally involve comparing the spectrometric data to one ormore comparator spectrometric data. If the spectrometric data obtainedfrom a sample matches or corresponds sufficiently to a comparatorspectrometric data, then optionally a positive determination may bemade. If the spectrometric data obtained from a sample does not match orcorrespond sufficiently to a comparator spectrometric data, thenoptionally a negative determination may be made.

The term “control” spectrometric data is used herein to meanspectrometric data obtained from the first sample at an earlier point intime. Control spectrometric data may, for example, be used whenmonitoring a microbial culture. Any of the methods provided herein mayoptionally involve comparing the spectrometric data to one or morecontrol spectrometric data. If the spectrometric data obtained from asample matches or corresponds sufficiently to a control spectrometricdata, then optionally a positive determination may be made. If thespectrometric data obtained from a sample does not match or correspondsufficiently to a control spectrometric data, then optionally a negativedetermination may be made.

By a “positive determination” is meant that the presence, identityand/or characteristics of a particular microbe and/or compound isdetermined. For example, a positive determination may involvedetermining that a microbe of a particular taxonomic rank is present;that a particular microbe has a certain characteristic, such as,resistance to a particular drug; and/or that a particular compound isbeing produced.

Thus, for example, if the spectrometric data of a first sample matchesor corresponds sufficiently to a reference spectrometric data, then thepresence in the first sample of a microbe corresponding to the microbefrom which the reference spectrometric data was obtained may beconfirmed. If the spectrometric data of a first sample matches orcorresponds sufficiently to a reference spectrometric data, then themicrobe present in the first sample may be identified as correspondingto the identity of the microbe from which the reference spectrometricdata was obtained. If the spectrometric data of a first sample matchesor corresponds sufficiently to a reference spectrometric data, then themicrobe present in the first sample may be characterised as having acharacteristic corresponding to the characteristic of the microbe fromwhich the reference spectrometric data was obtained. If thespectrometric data of a first sample matches or corresponds sufficientlyto a reference spectrometric data, then a determination may be made thatthe microbe present in the first sample produces the compound producedby the microbe from which the reference spectrometric data was obtained.

As another example, if the spectrometric data of a first sample matchesor corresponds sufficiently to a comparator spectrometric data, then thepresence in the first sample of a microbe corresponding to the microbefrom which the comparator spectrometric data was obtained may beconfirmed. If the spectrometric data of a first sample matches orcorresponds sufficiently to a comparator spectrometric data, then themicrobe present in the first sample may be identified as correspondingto the identity of the microbe from which the comparator spectrometricdata was obtained. If the spectrometric data of a first sample matchesor corresponds sufficiently to a comparator spectrometric data, then themicrobe present in the first sample may be characterised as having acharacteristic corresponding to the characteristic of the microbe fromwhich the comparator spectrometric data was obtained. If thespectrometric data of a first sample matches or corresponds sufficientlyto a comparator spectrometric data, then a determination may be madethat the microbe present in the first sample produces the compoundproduced by the microbe from which the comparator spectrometric data wasobtained.

In other words, a match or sufficient correspondence to a reference orcomparator spectrometric data respectively may be used to confirm thatthe first microbe and the reference or comparator microbe respectivelyhave the same identity, whereas the lack of a match or sufficientcorrespondence to a reference or comparator spectrometric datarespectively may be used to confirm that the first microbe and thereference or comparator microbe respectively do not have the sameidentity.

By a “negative determination” is meant that the absence of a particularmicrobe and/or compound is determined; and/or that it is determined thata microbe does not have a particular identity and/or characteristic. Forexample, a negative determination may involve determining that a microbeof a particular taxonomic rank is not present; that a particular microbedoes not have a certain characteristic such as resistance to aparticular drug; and/or that a particular compound is not beingproduced.

Thus, for example, if the spectrometric data of a first sample does notmatch or have sufficient correspondence to a reference spectrometricdata, then the absence or insufficient presence in the first sample of amicrobe corresponding to the microbe from which the referencespectrometric data was obtained may be confirmed. If the spectrometricdata of a first sample does not match or have sufficient correspondenceto a reference spectrometric data, then the microbe present in the firstsample may be identified as not corresponding to the identity of themicrobe from which the reference spectrometric data was obtained. If thespectrometric data of a first sample does not match or have sufficientcorrespondence to a reference spectrometric data, then the microbepresent in the first sample may be characterised as not having acharacteristic corresponding to the characteristic of the microbe fromwhich the reference spectrometric data was obtained. If thespectrometric data of a first sample does not match or have sufficientcorrespondence to a reference spectrometric data, then a determinationmay be made that the microbe present in the first sample does notproduce, or insufficiently produces, the compound produced by themicrobe from which the reference spectrometric data was obtained.

As explained below, by determining or confirming the “identity” of amicrobe is meant that at least some information about the identity isobtained, which may, for example, be at any taxonomic level. Thus, forexample, if the reference spectrometric data is from Candida albicans,then in one embodiment a match or sufficient correspondence may be usedto identify the first microbe as belonging to the genus Candida, whereasin another embodiment a match or sufficient correspondence may be usedto identify the first microbe as belonging to the species Candidaalbicans.

As another example, if the spectrometric data of a first sample matchesor sufficiently corresponds to a control spectrometric data, then adetermination may be made that no, or no significant, change has takenplace, whereas if the spectrometric data of a first sample does notmatch or correspond sufficiently to a control spectrometric data, then adetermination may be made that a change, optionally a significantchange, has taken place. Examples of a change may, for example, be thepresence of a contaminating microbe and/or compound; or a change in themicrobe's behaviour or its environment, such as, a change in themicrobe's growth rate, respiration rate; rate of production of acompound, such a secreted compound; environmental temperature, pH,nutrient availability and so on.

Optionally, the analyte giving rise to a particular spectrometricsignal, e.g., a particular m/z, may optionally be further characterised,e.g., using MS/MS. Thus, ionic species in the mass spectra mayoptionally be identified based on exact mass measurements, e.g., with amass deviation <3 ppm, and/or MS/MS fragmentation patterns. Isobariclipids with different headgroups may optionally be differentiated by ionmobility.

Thus, optionally, the method may involve analysing the target for thepresence of a spectrometric signal of one or more biomarkers, optionallyselected from any of the biomarkers mentioned herein.

The spectrometric data may comprise one or more sample spectra.Obtaining the spectrometric data may comprise obtaining the one or moresample spectra. Analysing the spectrometric data may comprise analysingthe one or more spectra. Obtaining the one or more sample spectra maycomprise a binning process to derive a set of time-intensity pairsand/or a set of sample intensity values for the one or more samplespectra. The binning process may comprise accumulating or histogrammingion detections and/or intensity values in a set of plural bins. Each binin the binning process may correspond to particular range of times ortime-based values, such as masses, mass to charge ratios, and/or ionmobilities. The bins in the binning process may each have a widthequivalent to a width in Da or Th (Da/e) in a range selected from agroup consisting of: (i) < or >0.01; (ii) 0.01-0.05; (iii) 0.05-0.25;(iv) 0.25-0.5; (v) 0.5-1.0; (vi) 1.0-2.5; (vii) 2.5-5.0; and (viii) <or >5.0. It has been identified that bins having widths equivalent towidths in the range 0.01-1 Da or Th (Da/e) can provide particularlyuseful sample spectra for classifying some aerosol, smoke or vapoursamples, such as samples obtained from tissues. The bins may or may notall have the same width. The widths of the bin in the binning processmay vary according to a bin width function. The bin width function mayvary with a time or time-based value, such as mass, mass to charge ratioand/or ion mobility. The bin width function may be non-linear (e.g.,logarithmic-based or power-based, such as square or square-root based).The bin width function may take into account the fact that the time offlight of an ion may not be directly proportional to its mass, mass tocharge ratio, and/or ion mobility. For example, the time of flight of anion may be directly proportional to the square-root of its mass and/ormass to charge ratio.

Spectrometric Library

The terms “spectrometric library” and “spectrometric database” are usedinterchangeably herein.

The skilled person may use any publicly available spectrometric data asreference spectrometric data. Examples of useful databases are:LipidMaps, LipidBlast and LipidXplorer, details of which are provided inthe following publications: “LipidBlast—in-silico tandem massspectrometry database for lipid identification” by Kind et al., NatMethods. 2013 August; 10(8): 755-758; “LipidXplorer: A Software forConsensual Cross-Platform Lipidomics” by Herzog et al. PLoS ONE 7(1):e29851; and “Lipid classification, structures and tools” by Fahy et al.Biochimica et Biophysica Acta (BBA)—Molecular and Cell Biology ofLipids, Volume 1811, Issue 11, November 2011, Pages 637-647, Lipidomicsand Imaging Mass Spectrometry, see also http://www.lipidmaps.org/.

Alternatively or in addition, the skilled person may construct aspectrometric library by obtaining spectrometric data from one or moresamples, which may optionally include type culture strains and/orclinical and/or environmental microbial isolates; in the case ofcompound, the sample(s) may optionally be purchased or synthesised.

Type culture strains may optionally be obtained from culturecollections, such as, the American Type Culture Collection (ATCC) (10801University Boulevard, Manassas, Va. 20110 USA).

The present inventors generated a spectrometric library using over 1500microbial strains, including clinical isolates and type culture strainsfrom the ATCC, encompassing about 95 genera and about 260 species ofbacteria and fungi. To expedite the generation of the spectrometriclibrary, the inventors set up high throughput culturing, automatedcolony imaging, colony picking and REIMS analysis.

The present inventors have also generated spectrometric libraries usingtissues and/or cell lines, details of which are provided elsewhereherein, including in the Examples.

The generation of a spectrometric library from microbes, cell linesand/or tissues may optionally be combined with a further analysis, e.g.,taxonomic classification and/or histology, e.g., based on any of thefurther analytical tools discussed elsewhere herein. For example, thetool may be DNA analysis. This may involve DNA sequencing, optionallypreceded by DNA isolation and/or amplification using, e.g., PCR. Forbacteria, sequencing of all or part of the 16S rRNA gene is particularlysuitable, whereas for fungi, sequencing of all or part of the internaltranscribed spacer (ITS) region is particularly suitable.

Analysing Sample Spectra

The step of analysing the spectrometric data may comprise analysing oneor more sample spectra so as to classify an aerosol, smoke or vapoursample.

Analysing the one or more sample spectra so as to classify the aerosol,smoke or vapour sample may comprise unsupervised analysis of the one ormore sample spectra (e.g., for dimensionality reduction) and/orsupervised analysis of the one or more sample spectra (e.g., forclassification).

Analysing the one or more sample spectra may comprise unsupervisedanalysis (e.g., for dimensionality reduction) followed by supervisedanalysis (e.g., for classification).

Analysing the one or more sample spectra may be performed as discussedelsewhere herein.

A list of analysis techniques which are intended to fall within thescope of the present invention are given in the following table:

Analysis Techniques Univariate Analysis Multivariate Analysis PrincipalComponent Analysis (PCA) Linear Discriminant Analysis (LDA) MaximumMargin Criteria (MMC) Library Based Analysis Soft Independent ModellingOf Class Analogy (SIMCA) Factor Analysis (FA) Recursive Partitioning(Decision Trees) Random Forests Independent Component Analysis (ICA)Partial Least Squares Discriminant Analysis (PLS-DA) Orthogonal (PartialLeast Squares) Projections To Latent Structures (OPLS) OPLS DiscriminantAnalysis (OPLS-DA) Support Vector Machines (SVM) (Artificial) NeuralNetworks Multilayer Perceptron Radial Basis Function (RBF) NetworksBayesian Analysis Cluster Analysis Kernelized Methods SubspaceDiscriminant Analysis K-Nearest Neighbours (KNN) Quadratic DiscriminantAnalysis (QDA) Probabilistic Principal Component Analysis (PPCA) Nonnegative matrix factorisation K-means factorisation Fuzzy c-meansfactorisation Discriminant Analysis (DA)

Combinations of the foregoing analysis approaches can also be used, suchas PCA-LDA, PCA-MMC, PLS-LDA, etc.

Analysing the sample spectra can comprise unsupervised analysis fordimensionality reduction followed by supervised analysis forclassification.

By way of example, a number of different analysis techniques will now bedescribed in more detail.

Multivariate Analysis—Developing a Model for Classification

By way of example, a method of building a classification model usingmultivariate analysis of plural reference sample spectra will now bedescribed.

FIG. 15 shows a method 1500 of building a classification model usingmultivariate analysis. In this example, the method comprises a step 1502of obtaining plural sets of intensity values for reference samplespectra. The method then comprises a step 1504 of unsupervised principalcomponent analysis (PCA) followed by a step 1506 of supervised lineardiscriminant analysis (LDA). This approach may be referred to herein asPCA-LDA. Other multivariate analysis approaches may be used, such asPCA-MMC. The PCA-LDA model is then output, for example to storage, instep 1508.

The multivariate analysis such as this can provide a classificationmodel that allows an aerosol, smoke or vapour sample to be classifiedusing one or more sample spectra obtained from the aerosol, smoke orvapour sample. The multivariate analysis will now be described in moredetail with reference to a simple example.

FIG. 16 shows a set of reference sample spectra obtained from twoclasses of known reference samples. The classes may be any one or moreof the classes of target described herein. However, for simplicity, inthis example the two classes will be referred as a left-hand class and aright-hand class.

Each of the reference sample spectra has been pre-processed in order toderive a set of three reference peak-intensity values for respectivemass to charge ratios in that reference sample spectrum. Although onlythree reference peak-intensity values are shown, it will be appreciatedthat many more reference peak-intensity values (e.g., ˜100 referencepeak-intensity values) may be derived for a corresponding number of massto charge ratios in each of the reference sample spectra. In otherembodiments, the reference peak-intensity values may correspond to:masses; mass to charge ratios; ion mobilities (drift times); and/oroperational parameters.

FIG. 17 shows a multivariate space having three dimensions defined byintensity axes. Each of the dimensions or intensity axes corresponds tothe peak-intensity at a particular mass to charge ratio. Again, it willbe appreciated that there may be many more dimensions or intensity axes(e.g., ˜100 dimensions or intensity axes) in the multivariate space. Themultivariate space comprises plural reference points, with eachreference point corresponding to a reference sample spectrum, i.e., thepeak-intensity values of each reference sample spectrum provide theco-ordinates for the reference points in the multivariate space.

The set of reference sample spectra may be represented by a referencematrix D having rows associated with respective reference samplespectra, columns associated with respective mass to charge ratios, andthe elements of the matrix being the peak-intensity values for therespective mass to charge ratios of the respective reference samplespectra.

In many cases, the large number of dimensions in the multivariate spaceand matrix D can make it difficult to group the reference sample spectrainto classes. PCA may accordingly be carried out on the matrix D inorder to calculate a PCA model that defines a PCA space having a reducednumber of one or more dimensions defined by principal component axes.The principal components may be selected to be those that comprise or“explain” the largest variance in the matrix D and that cumulativelyexplain a threshold amount of the variance in the matrix D.

FIG. 18 shows how the cumulative variance may increase as a function ofthe number n of principal components in the PCA model. The thresholdamount of the variance may be selected as desired.

The PCA model may be calculated from the matrix D using a non-lineariterative partial least squares (NIPALS) algorithm or singular valuedecomposition, the details of which are known to the skilled person andso will not be described herein in detail. Other methods of calculatingthe PCA model may be used.

The resultant PCA model may be defined by a PCA scores matrix S and aPCA loadings matrix L. The PCA may also produce an error matrix E, whichcontains the variance not explained by the PCA model. The relationshipbetween D, S, L and E may be:

D=SL ^(T) +E   (1)

FIG. 19 shows the resultant PCA space for the reference sample spectraof FIGS. 16 and 17 . In this example, the PCA model has two principalcomponents PC₀ and PC₁ and the PCA space therefore has two dimensionsdefined by two principal component axes. However, a lesser or greaternumber of principal components may be included in the PCA model asdesired. It is generally desired that the number of principal componentsis at least one less than the number of dimensions in the multivariatespace.

The PCA space comprises plural transformed reference points or PCAscores, with each transformed reference point or PCA score correspondingto a reference sample spectrum of FIG. 16 and therefore to a referencepoint of FIG. 17 .

As is shown in FIG. 19 , the reduced dimensionality of the PCA spacemakes it easier to group the reference sample spectra into the twoclasses. Any outliers may also be identified and removed from theclassification model at this stage.

Further supervised multivariate analysis, such as multi-class LDA ormaximum margin criteria (MMC), in the PCA space may then be performed soas to define classes and, optionally, further reduce the dimensionality.

As will be appreciated by the skilled person, multi-class LDA seeks tomaximise the ratio of the variance between classes to the variancewithin classes (i.e., so as to give the largest possible distancebetween the most compact classes possible). The details of LDA are knownto the skilled person and so will not be described herein in detail.

The resultant PCA-LDA model may be defined by a transformation matrix U,which may be derived from the PCA scores matrix S and class assignmentsfor each of the transformed spectra contained therein by solving ageneralised eigenvalue problem.

The transformation of the scores S from the original PCA space into thenew LDA space may then be given by:

Z=SU   (2)

where the matrix Z contains the scores transformed into the LDA space.

FIG. 20 shows a PCA-LDA space having a single dimension or axis, whereinthe LDA is performed in the PCA space of FIG. 19 . As is shown in FIG.20 , the LDA space comprises plural further transformed reference pointsor PCA-LDA scores, with each further transformed reference pointcorresponding to a transformed reference point or PCA score of FIG. 19 .

In this example, the further reduced dimensionality of the PCA-LDA spacemakes it even easier to group the reference sample spectra into the twoclasses. Each class in the PCA-LDA model may be defined by itstransformed class average and covariance matrix or one or morehyperplanes (including points, lines, planes or higher orderhyperplanes) or hypersurfaces or Voronoi cells in the PCA-LDA space.

The PCA loadings matrix L, the LDA matrix U and transformed classaverages and covariance matrices or hyperplanes or hypersurfaces orVoronoi cells may be output to a database for later use in classifyingan aerosol, smoke or vapour sample.

The transformed covariance matrix in the LDA space V′_(g) for class gmay be given by

V′_(g)=U^(T) V_(g) U   (3)

where V_(g) are the class covariance matrices in the PCA space.

The transformed class average position z_(g) for class g may be given by

s_(g)U=z_(g)   (4)

where s_(g) is the class average position in the PCA space.

Multivariate Analysis—Using a Model for Classification

By way of example, a method of using a classification model to classifyan aerosol, smoke or vapour sample will now be described.

FIG. 21 shows a method 2100 of using a classification model. In thisexample, the method comprises a step 2102 of obtaining a set ofintensity values for a sample spectrum. The method then comprises a step2104 of projecting the set of intensity values for the sample spectruminto PCA-LDA model space. Other classification model spaces may be used,such as PCA-MMC. The sample spectrum is then classified at step 2106based on the project position and the classification is then output instep 2108.

Classification of an aerosol, smoke or vapour sample will now bedescribed in more detail with reference to the simple PCA-LDA modeldescribed above.

FIG. 22 shows a sample spectrum obtained from an unknown aerosol, smokeor vapour sample. The sample spectrum has been pre-processed in order toderive a set of three sample peak-intensity values for respective massto charge ratios. As mentioned above, although only three samplepeak-intensity values are shown, it will be appreciated that many moresample peak-intensity values (e.g., ˜100 sample peak-intensity values)may be derived at many more corresponding mass to charge ratios for thesample spectrum. Also, as mentioned above, in other embodiments, thesample peak-intensity values may correspond to: masses; mass to chargeratios; ion mobilities (drift times); and/or operational parameters.

The sample spectrum may be represented by a sample vector d_(x), withthe elements of the vector being the peak-intensity values for therespective mass to charge ratios. A transformed PCA vector s_(X) for thesample spectrum can be obtained as follows:

d_(x)L=s_(x)   (5)

Then, a transformed PCA-LDA vector z_(X) for the sample spectrum can beobtained as follows:

s_(x)U=z_(x)   (6)

FIG. 23 again shows the PCA-LDA space of FIG. 20 . However, the PCA-LDAspace of FIG. 23 further comprises the projected sample point,corresponding to the transformed PCA-LDA vector z_(x), derived from thepeak intensity values of the sample spectrum of FIG. 22 .

In this example, the projected sample point is to one side of ahyperplane between the classes that relates to the right-hand class, andso the aerosol, smoke or vapour sample may be classified as belonging tothe right-hand class.

Alternatively, the Mahalanobis distance from the class centres in theLDA space may be used, where the Mahalanobis distance of the point z_(x)from the centre of class g may be given by the square root of:

(z_(x)−z_(g))^(T)(V′_(g))⁻¹(z_(x)−z_(g))   (8)

and the data vector d_(x) may be assigned to the class for which thisdistance is smallest.

In addition, treating each class as a multivariate Gaussian, aprobability of membership of the data vector to each class may becalculated.

Library Based Analysis—Developing a Library for Classification

By way of example, a method of building a classification library usingplural input reference sample spectra will now be described.

FIG. 24 shows a method 2400 of building a classification library. Inthis example, the method comprises a step 2402 of obtaining plural inputreference sample spectra and a step 2404 of deriving metadata from theplural input reference sample spectra for each class of sample. Themethod then comprises a step 2406 of storing the metadata for each classof sample as a separate library entry. The classification library isthen output, for example to electronic storage, in step 2408.

A classification library such as this allows an aerosol, smoke or vapoursample to be classified using one or more sample spectra obtained fromthe aerosol, smoke or vapour sample. The library based analysis will nowbe described in more detail with reference to an example.

In this example, each entry in the classification library is createdfrom plural pre-processed reference sample spectra that arerepresentative of a class. In this example, the reference sample spectrafor a class are pre-processed according to the following procedure:

First, a re-binning process is performed. In this embodiment, the dataare resampled onto a logarithmic grid with abscissae:

$x_{i} = \left\lfloor {N_{chan}\log\frac{m}{M_{\min}}/\log\frac{M_{\max}}{M_{\min}}} \right\rfloor$

where N_(chan) is a selected value and └x┘ denotes the nearest integerbelow x. In one example, N_(chan) is 2¹² or 4096.

Then, a background subtraction process is performed. In this embodiment,a cubic spline with k knots is then constructed such that p % of thedata between each pair of knots lies below the curve. This curve is thensubtracted from the data. In one example, k is 32. In one example, p is5. A constant value corresponding to the q % quantile of the intensitysubtracted data is then subtracted from each intensity. Positive andnegative values are retained. In one example, q is 45.

Then, a normalisation process is performed. In this embodiment, the dataare normalised to have mean y _(i). In one example, y _(i)=1.

An entry in the library then consists of metadata in the form of amedian spectrum value μ_(i) and a deviation value D_(i) for each of theN_(chan) points in the spectrum.

The likelihood for the i'th channel is given by:

${\Pr\left( {y_{i}{❘{\mu_{i},D_{i}}}} \right)} = {\frac{1}{D_{i}}\frac{C^{C - {1/2}}{\Gamma(C)}}{\sqrt{\pi}{\Gamma\left( {C - {1/2}} \right)}}\frac{1}{\left( {C + \frac{\left( {y_{i} - \mu_{i}} \right)^{2}}{D_{i}^{2}}} \right)^{C}}}$

where 1/2≤C<∞ and where Γ(C) is the gamma function.

The above equation is a generalised Cauchy distribution which reduces toa standard Cauchy distribution for C=1 and becomes a Gaussian (normal)distribution as C→∞. The parameter D_(i) controls the width of thedistribution (in the Gaussian limit D_(i)=σ_(i) is simply the standarddeviation) while the global value C controls the size of the tails.

In one example, C is 3/2, which lies between Cauchy and Gaussian, sothat the likelihood becomes:

${\Pr\left( {y_{i}{❘{\mu_{i},D_{i}}}} \right)} = {\frac{3}{4}\frac{1}{D_{i}}\frac{1}{\left( {{3/2} + {\left( {y_{i} - \mu_{i}} \right)^{2}/D_{i}^{2}}} \right)^{3/2}}}$

For each library entry, the parameters μ_(i) are set to the median ofthe list of values in the i'th channel of the input reference samplespectra while the deviation D_(i) is taken to be the interquartile rangeof these values divided by √2. This choice can ensure that thelikelihood for the i'th channel has the same interquartile range as theinput data, with the use of quantiles providing some protection againstoutlying data.

Library-Based Analysis—Using a Library for Classification

By way of example, a method of using a classification library toclassify an aerosol, smoke or vapour sample will now be described.

FIG. 25 shows a method 2500 of using a classification library. In thisexample, the method comprises a step 2502 of obtaining a set of pluralsample spectra. The method then comprises a step 2504 of calculating aprobability or classification score for the set of plural sample spectrafor each class of sample using metadata for the class entry in theclassification library. The sample spectra are then classified at step2506 and the classification is then output in step 2508.

Classification of an aerosol, smoke or vapour sample will now bedescribed in more detail with reference to the classification librarydescribed above.

In this example, an unknown sample spectrum y is the median spectrum ofa set of plural sample spectra. Taking the median spectrum y can protectagainst outlying data on a channel by channel basis.

The likelihood L_(s) for the input data given the library entry s isthen given by:

$L_{s} = {{\Pr\left( {y{❘{\mu,D}}} \right)} = {\prod\limits_{i = 1}^{N_{chan}}{\Pr\left( {y_{i}{❘{\mu_{i},D_{i}}}} \right)}}}$

where μ_(i) and D_(i) are, respectively, the library median values anddeviation values for channel i. The likelihoods L_(s) may be calculatedas log likelihoods for numerical safety.

The likelihoods L_(s) are then normalised over all candidate classes ‘s’to give probabilities, assuming a uniform prior probability over theclasses. The resulting probability for the class s is given by:

${\Pr\left( {\overset{\sim}{s}{❘y}} \right)} = \frac{L_{\overset{\sim}{S}}^{({1/F})}}{\sum_{S}L_{S}^{({1/F})}}$

The exponent (1/F) can soften the probabilities which may otherwise betoo definitive. In one example, F=100. These probabilities may beexpressed as percentages, e.g., in a user interface.

Alternatively, RMS classification scores R_(s) may be calculated usingthe same median sample values and derivation values from the library:

${R_{s}\left( {y,\mu,D} \right)} = \sqrt{\frac{1}{N_{chan}}{\sum\limits_{i = 1}^{N_{chan}}\frac{\left( {y_{i} - \mu_{i}} \right)^{2}}{D_{i}^{2}}}}$

Again, the scores R_(s) are normalised over all candidate classes ‘s’.

The aerosol, smoke or vapour sample may then be classified as belongingto the class having the highest probability and/or highest RMSclassification score.

Microbial Detection, Identification and/or Characterisation

The terms “detect” and “detection” and derivations of these terms areused interchangeably herein to mean that the presence or absence of atarget entity is determined.

Any of the methods in which the target entity is a microbe mayoptionally be used to determine whether or not a particular target orregion thereof is sterile or non-sterile. Thus, in one embodiment, amicrobe is detected and optionally based on this detection adetermination is made that the target or region is non-sterile. In oneembodiment, a microbe is not detected and optionally based on thisdetection a determination is made that the target or region is sterile.

Any of the methods in which the target entity is a microbe may be usedto determine that one or more different types of microbes are present.The detection of only 1 type of microbe may indicate that no other typesof microbes are present in the sample. Thus, in one embodiment, thepresence of only 1 type of microbe is determined. In the case of asample from a microbial culture, optionally a determination is made thata microbial culture is free of contaminating microbes. In the case of aclinical or food sample, optionally a determination is made that aninfection is, or is likely to be, only caused by a single microbialpathogen.

In one embodiment, the presence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 25, 30, 35, 40, 45 or 50 different types of microbes is determined.In one embodiment, the presence of 1 or more, 2 or more, 3 or more, 4 ormore, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more,15 or more, 25 or more or 30 or more different types of microbes isdetermined. In the case of a sample from a microbial culture, optionallya determination is made that a microbial culture contains contaminatingmicrobes. In the case of a clinical or food sample, optionally adetermination is made that an infection is, or is likely to be, causedby a plurality of microbes.

Some microbes are pathogenic, whereas others are non-pathogenic. Apathogenic microbe may be defined as a microbe that is able to causedisease in a host, such as a plant or animal. A pathogen may be anobligate pathogen or an opportunistic pathogen.

The ability of a microbe to cause disease depends both on its intrinsicvirulence factors and on the ability of the host to fight off themicrobe. The distinction between non-pathogens and opportunisticpathogens is therefore not clear-cut, because, for example,immuno-compromised hosts will be susceptible to infection by microbesthat may be unable to infect a host with a healthy immune system.Antibiotic use can also create an environment in which a microbe willflourish as an opportunistic pathogen.

For example, Neisseria gonorrhoeae is an obligate pathogen, Pseudomonasaeruginosa and Candida albicans are typically referred to asopportunistic pathogens, and Lactobacillus acidophilus andBifidobacterium bifidum are typically considered to be non-pathogens or“commensal” bacteria.

Pathogenic microbes may optionally be characterised by the expression ofone or more virulence factors, i.e. factors that allow or facilitateinfection of a host. Virulence factors may optionally be selected fromfactors that mediate cell adherence, cell growth, the ability to bypassor overcome host defence mechanisms, and/or the production of toxins.Toxins may be selected from exotoxins and endotoxins.

Thus, the method may be used to determine whether one or more pathogenicmicrobes are present. In one embodiment, the presence of 1 or more, 2 ormore, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more,9 or more, 10 or more, 15 or more, 25 or more or 30 or more differentpathogenic microbes is determined.

It is often desirable to identify a microbial population present in oron a target. The identity of microbial populations in/on clinical and/orenvironmental specimens is often unknown and identification is thereforedesired. With regard to microbial cultures, human error and/or othercircumstances can lead to misidentification, mislabelling, mix-ups, andthe like, of microbial populations.

Thus, the identity of a particular microbial population may be unknown,uncertain and/or unconfirmed. Optionally, the method may be used toidentify a microbial population, and/or to confirm the identity of amicrobial population.

The terms “determine identity” “identify” and “identification” andderivations of these terms are used interchangeably herein

By “identifying” a microbial population is meant that at least someinformation about the type(s) of microbes present in the microbialpopulation is obtained. This may optionally be the determination of theidentity, and/or the confirmation of the identity of one or moremicrobial types in the microbial population. Confirming the identity ofa microbial population may, e.g., in the context of a microbial culture,also be referred to as confirming the authenticity of a microbialpopulation. However, “identifying” may alternatively include informationthat allows the microbe to be identified as falling into a particularclassification.

Thus, optionally the method may be performed on a microbial populationwhose identity is unknown, to determine the identity of the microbialpopulation.

Optionally, the method may be performed on a microbial populationsuspected of having a particular identity, to confirm or refute theidentity of the microbial population.

Optionally, the method may be performed on a microbial population inneed of authentication, to confirm the authenticity of the microbialpopulation.

Optionally, the microbial population may be identified as comprising orconsisting of one or more of the microbes listed elsewhere herein, e.g.,having the identity of any one of the microbes listed elsewhere herein.

Optionally, the microbial population may be identified as comprising orconsisting of pathogenic microbes. Optionally, the microbial populationmay be identified as comprising or consisting of commensal microbes,e.g., non-pathogenic microbes.

Optionally, the microbial population may be identified as comprising orconsisting of mutant and/or transgenic microbes.

Optionally, the microbial population may be identified as comprising orconsisting of microbes that are sensitive and/or resistant to one ormore antimicrobials.

Optionally, the microbial population may be analysed (i) to confirm theidentity or authenticity of said microbial population; (ii) to detect amutation in said microbial population; and/or (iii) to detect anundesired variation in said microbial population.

Optionally, the method may be used to analyse the spread of aninfection. For example, the method may be used to analyse whether two ormore different subjects have been infected by the same type of microbeor by two different types of microbes. This may optionally involve theanalysis of the genotype and/or phenotype of the microbe, particularlyif each infection is caused by the same microbial species. Thus,optionally, the method may be used to determine the identity and/orgenotype and/or phenotype of a first microbial population causing aninfection in a first subject, as well as the identity and/or genotypeand/or phenotype of a second or further microbial population causing aninfection in a second or further subject. A determination may then bemade as to whether the first and the second or further microbialpopulation are identical, and, if not, to what extent or howsignificantly they differ from one another. If a determination is madethat the first and the second microbial population are identical orsignificantly similar, then optionally a determination may be made thatthe first and the second subject have, or are likely to have, acquiredthe same infection, e.g., acquired the infection from the same source.Consersely, if a determination is made that the first and the secondmicrobial population are not identical or significantly similar, thenoptionally a determination may be made that the first and the secondsubject have, or are likely to have, acquired different infections,e.g., acquired the infection from different sources. This may, e.g, helpto analyse whether a patient acquired an infection prior to entering ahospital, or whether a patient has a hospital-acquired infection.

Microbial identification may optionally be on any taxonomic level, forexample, at the Kingdom, Phylum or Division, Class, Order, Family,Genus, Species and/or Strain level.

“Taxonomy” is the classification of organisms, and each level ofclassification may be referred to as a “taxon” (plural: taxa). Organismsmay be classified into the following taxa in increasing order ofspecificity: Kingdom, Phylum or Division, Class, Order, Family, Genus,Species and Strain. Further subdivisions of each taxon may exist. Itmust be appreciated that within the vast scientific community there aresome discrepancies within some taxonomic classifications. There may alsobe a lack of consensus with regard to the nomenclature of certainmicrobes, resulting in a particular microbe having more than one name orin two different microbes having the same name.

Taxonomic classification is illustrated below by reference toPseudomonas aeruginosa, which belongs to the Kingdom: bacteria; Phylum:Proteobacteria; Class: Gammaproteobacteria; Order: Pseudomonadales;Family: Pseudomonadaceae; Genus: Pseudomonas; and Species: Pseudomonasaeruginosa.

A microbe, also known as a micro-organism, is an organism which is toosmall to be visible to the naked eye, i.e. is microscopic. A microbe maybe selected from bacteria, fungi, archaea, algae, protozoa, and viruses.Although the terms bacteria, fungi, archaea, algae, protozoa and virusestechnically denote the plural form, it is common practice to use themalso to denote the singular form. Consequently, the terms “bacteria” and“bacterium” are used interchangeably herein; the terms “fungi” and“fungus” are used interchangeably herein; the terms “archaea” and“archaeum” are used interchangeably herein; the terms “protozoa” and“protozoum” are used interchangeably herein; and the terms “viruses” and“virus” are used interchangeably herein.

In some embodiments, the microbe may be selected from bacteria, fungi,archaea, algae and protozoa. In some embodiments, it may be selectedfrom bacteria and fungi. In some embodiments, it may be selected frombacteria.

The microbe may be single-cellular or multi-cellular. If the microbe isa fungus, it may optionally be filamentous or single-cellular, e.g., ayeast.

A fungus may optionally be yeast. It may optionally be selected from thegenus Aspergillus, Arthroascus, Brettanomyces Candida, Cryptococcus,Debaryomyces, Geotrichum, Pichia, Rhodotorula, Saccharomyces,Trichosporon, and Zygotorulaspora.

It may optionally be selected from the species Arthroascus schoenii,Brettanomyces bruxellensis, Candida albicans, C. ascalaphidarum, C.amphixiae, C. antarctica, C. argentea, C. atlantica, C. atmosphaerica,C. blattae, C. bromeliacearum, C. carpophila, C. carvajalis, C.cerambycidarum, C. chauliodes, C. corydali, C. dosseyi, C. dubliniensis,C. ergatensis, C. fructus, C. glabrata, C. fermentati, C.guilliermondii, C. haemulonii, C. insectamens, C. insectorum, C.intermedia, C. jeffresii, C. kefyr, C. keroseneae, C. krusei, C.lusitaniae, C. lyxosophila, C. maltosa, C. marina, C. membranifaciens,C. milleri, C. mogii, C. oleophila, C. oregonensis, C. parapsilosis, C.quercitrusa, C. rugosa, C. sake, C. shehatea, C. temnochilae, C. tenuis,C. theae, C. tolerans, C. tropicalis, C. tsuchiyae, C. sinolaborantium,C. sojae, C. subhashii, C. viswanathii, C. utilis, C. ubatubensis, C.zemplinina, Cryptococcus neoformans, Cryptococcus uniguttulatus,Debaryomyces carsonii, Geotrichum capitatum, Trichosporon asahii,Trichosporon mucoides, Trichosporon inkin, Saccharomyces cerevisiae,Pichia acaciae, Pichia anomala, Pichia capsulata, Pichia farinosa,Pichia guilliermondii, Pichia spartinae, Pichia ohmeri, Rhodotorulaglutinous, Rhodotorula mucilaginosa, Saccharomyces boulardii,Saccharomyces cerevisiae, and/or Zygotorulaspora florentinus.

The protozoa may be selected from the group of amoebae, flagellates,ciliates or sporozoa. It may be selected from the genus Acanthamoeba,Babesia, Balantidium, Cryptosporidium, Dientamoeba, Entamoeba, Giardia,Leishmania, Naegleria, Plasmodium Paramecium, Trichomonas, Trypanosoma,Typanosoma, Toxoplasma

The protozoa may be of the species Balantidium coli, Entamoebahistolytica, Giardia lamblia (also known as Giardia intestinalis, orGiardia duodenalis), Leishmania donovani, L. tropica, L. brasiliensis,Plasmodium falciparum, P. vivax, P. ovale, P. malariae, P. knowlesi, P.reichenowi, P. gaboni, P. mexicanum, P. floridense Trypanosoma brucei,Typanosoma evansi, Trypanosoma rhodesiense, Trypanosoma cruzi,Toxoplasma gondii.

The algae may optionally be selected from Chlamydomonas.

The bacteria may optionally be selected from the phylum Aquficae,Thermotogae, Thermodesulfobacteria, Deinococcus-Thermus, Chrysiogenetes,Chloroflexi, Thermomicrobia, Nitrospira, Deferribacteres, Cyanobacteria,Chlorobi, Proteobacteria, Firmicutes, Actinobacteria, Planctomycetes,Chlamydiae, Spirochaetes, Fibrobacteres, Acidobacteria, Bacteroidetes,Fusobacteria, Verrucomicrobia, Dictyoglomi, Gemmatomonadetes, andLentisphaerae.

The bacteria may optionally be selected from the class Actinobacteria,Alphaproteobacteria, Bacilli, Betaproteobacteria, Clostridia,Deltaproteobacteria, Epsilonproteobacteria, Flavobacteriaceae,Fusobacteria, Gammaproteobacteria, Mikeiasis, Mollicutes, orNegativicutes.

The bacteria may optionally be of the Order Aeromonadales,Actinomycetales, Bacillales, Bacteroidales, Bifidobacteriales,Burkholderiales, Campylobacterales, Caulobacterales, Cardiobacteriales,Clostridiales, Enterobacteriales, Flavobacteriales, Fusobacteriales,Lactobacillales, Micrococcales, Neisseriales, Pasteurellales,Pseudomonadales, Rhizobiales, Rhodospirillales, Selenomonadales,Vibrionales, Xanthomonadales.

The bacteria may optionally be selected from the FamilyAcetobacteraceae, Alcaligenaceae, Bacillaceae, Bacteroidaceae,Burkholderiaceae, Caulobacteraceae, Comamonadaceae, Enterobacteriaceae,Flavobacteriaceae, Fusobacteriaceae Nocardiaceae, Prevotellaceae,Porphyromonadaceae, Pseudomonadaceae, Rikenellaceae, Rhizobiaceae,Sutterellaceae.

The bacteria may optionally be of a genus selected from, e.g.,Abiotrophia, Achromobacter, Acidovorax, Acinetobacter, Actinobacillus,Actinomadura, Actinomyces, Aerococcus, Aeromonas, Anaerococcus,Anaplasma, Bacillus, Bacteroides, Bartonella, Bifidobacterium,Bordetella, Borrelia, Brevundimonas, Brucella, BurkholderiaCampylobacter, Capnocytophaga, Chlamydia, Citrobacter, Chlamydophila,Chryseobacterium, Clostridium, Comamonas, Corynebacterium, Coxiella,Cupriavidus, Delftia, Dermabacter, Ehrlichia, Eikenella, Enterobacter,Enterococcus, Escherichia, Erysipelothrix, Facklamia, Finegoldia,Francisella, Fusobacterium, Gemella, Gordonia, Haemophilus,Helicobacter, Klebsiella, Lactobacillus, Legionella, Leptospira,Listeria, Micrococcus, Moraxella, Morganella, Mycobacterium, Mycoplasma,Neisseria, Nocardia, Orientia, Pandoraea, Pasteurella, Peptoniphilus,Peptostreptococcus, Plesiomonas, Porphyromonas, Pseudomonas, Prevotella,Proteus, Propionibacterium, Rhodococcus, Ralstonia, Raoultella,Rickettsia, Rothia, Salmonella, Serratia, Shigella, Staphylococcus,Stenotrophomonas, Streptococcus, Tannerella, Treponema, Ureaplasma,Vibrio or Yersinia.

The bacteria may optionally be of a species selected from, e.g.,Abiotrophia defective, Achromobacter xylosoxidans, Acidovorax avenae,Acidovorax citrulli, Bacillus anthracis, B. cereus, B. subtilis, B.licheniformis, Bacteroides fragilis, Bartonella henselae, Bartonellaquintana, Bordetella pertussis, Borrelia burgdorferi, Borrelia garinii,Borrelia afzelii, Borrelia recurrentis, Brucella abortus, Brucellacanis, Brucella melitensis, Brucella suis, Burkholderia cepacia,Burkholderia genomovars, Campylobacter jejuni, Chlamydia pneumoniae,Chlamydia trachomatis, Chlamydophila psittaci, Citrobacter koseri,Clostridium botulinum, Clostridium difficile, C. perfringens, C. tetani,Corynebacterium diphtherias, C. striatum, C. minutissimum, C. imitans,C. amycolatum, Delftia acidovorans, Enterobacter aerogenes, E. cloacaeEnterococcus faecalis, Enterococcus faecium, Escherichia coli,Francisella tularensis, Fusobacterium nucleatum, Haemophilus influenzae,Helicobacter pylori, Klebsiella oxytoca, K. pneumonia, Legionellapneumophila, Leptospira interrogans, Leptospira santarosai, Leptospiraweilii, Leptospira noguchii, Listeria ivanovii, Listeria monocytogenes,Micrococcus luteus, Morganella morganii, Moraxella catarrhalis,Mycobacterium avium, M. fortuitum, M. leprae, M. peregrium, M.tuberculosis, M. ulcerans, Mycoplasma pneumoniae, Neisseria gonorrhoeae,N. lactamica, N. meningitidis, Nocardia asteroids, Proteus mirabilis,Pseudomonas aeruginosa, Rhodococcus equi, Rhodococcus pyridinivorans,Rickettsia rickettsii, Salmonella typhi, Salmonella typhimurium,Serratia marcescens, Shigella sonnei, Staphylococcus aureus, S. capitis,S. epidermidis, S. haemolyticus, S. hominis, S. saprophyticus,Stenotrophomonas maltophilia, Streptococcus agalactiae, S. pyogenes, S.pneumonia, Treponema pallidum, Ureaplasma urealyticum, Vibrio cholerae,Yersinia pestis, Yersinia enterocolitica and Yersiniapseudotuberculosis.

The virus may optionally be a DNA virus, and RNA virus or a retrovirus.It may optionally be a single stranded (ss) or a double stranded (ds)virus. More particularly, it may optionally be a ssDNA, dsDNA, dsRNA,ssRNA(positive strand), ssRNA (negative strand), ssRNA (reversetranscribed) or dsDNA (reverse transcribed) virus.

It may optionally be selected from one or more of the Herpesviridae,optionally selected from Simplexvirus, Varicellovirus, Cytomegalovirus,Roseolovirus, Lymphocryptovirus, and/or Rhadinovirus; the Adenoviridae,optionally selected from Adenovirus and/or Mastadenovirus;Papillomaviridae, optionally selected from Alphapapillomavirus,Betapapillomavirus, Gammapapilloma-virus, Mupapillomavirus, and/orNupapillomavirus; Polyomaviridae, optionally selected from Polyomavirus;Poxviridae, optionally selected from Molluscipoxvirus, Orthopoxvirusand/or Parapoxvirus; Anelloviridae, optionally selected fromAlphatorquevirus, Betatorquevirus, and/or Gammatorquevirus;Mycodnaviridae, optionally selected from Gemycircular-viruses;Parvoviridae, optionally selected from Erythrovirus, Dependovirus,and/or Bocavirus; Reoviridae, optionally selected from Coltivirus,Rotavirus, and/or Seadornavirus; Coronaviridae, optionally selected fromAlphacoronavirus, Betacoronavirus, and/or Torovirus; Astroviridae,optionally selected from Mamastrovirus; Caliciviridae, optionallyselected from Norovirus, and/or Sapovirus; Flaviviridae, optionallyselected from Flavivirus, Hepacivirus, and/or Pegivirus; Picornaviridae,optionally selected from Cardiovirus, Cosavirus, Enterovirus,Hepatovirus, Kobuvirus, Parechovirus, Rosavirus, and/or Salivirus;Togaviridae, optionally selected from Alphavirus and/or Rubivirus;Rhabdoviridae, optionally selected from Lyssavirus, and/orVesiculovirus; Filoviridae optionally selected from Ebolavirus, and/orMarburgvirus; Paramyxoviridae, optionally selected from Henipavirus,Morbilivirus, Respirovirus, Rubulavirus, Metapneumovirus, and/orPneumovirus; Arenaviridae, optionally selected from Arenavirus;Bunyaviridae, optionally selected from Hantavirus, Nairovirus,Orthobunyavirus, and/or Phlebovirus; Orthomyxoviridae, optionallyselected from Influenzavirus A, Influenzavirus B, Influenzavirus Cand/or Thogotovirus; Retroviridae, optionally selected fromGammaretrovirus, Deltaretrovirus, Lentivirus, Spumavirus; Epadnaviridae,optionally selected from Orthohepadnavirus; Hepevirus; and/orDeltavirus.

Thus, optionally, the spectrometric data may be used to microbe to beidentified as: (i) being a prokaryote or a eukaryote; (ii) belonging toa particular Kingdom, such as, any of those listed herein; (iii)belonging to a particular Phylum or Division, such as, any of thoselisted herein; (iv) belonging to a particular Class, such as, any ofthose listed herein; (v) belonging to a particular Order, such as, anyof those listed herein; (vi) belonging to a particular Family, such as,any of those listed herein; (vii) belonging to a particular Genus, suchas, any of those listed herein; (viii) belonging to a particularSpecies, such as, any of those listed herein; and/or (ix) belonging to aparticular Strain, such as, any of those listed herein.

The terms “characterise” and “characterisation” and derivations of theseterms are used interchangeably herein to mean that information about thecharacteristics of a target entity is obtained.

Information about the characteristics of a microbe may, for example, beselected from one or more of the following: virulence, antimicrobialsensitivity or resistance, ability to produce a particular compound,growth rate, production rate with regard to a particular compound,respiration rate, and/or response to/level of stress. Thus,characterisation may involve the analysis of the genotype and/orphenotype of a microbe, and/or the analysis of a property of a microbe.

Analysis of Phenotype, Genotype and/or Homogeneity

Genetic mutations may alter the structure of a protein, e.g., by codingfor a different amino acid, and/or by resulting in a shortened orelongated protein. Genetic mutations may alternatively or in additionresult in a reduced output or absence of a gene product.

The term “phenotype” is used to refer to the physical and/or biochemicalcharacteristics of a microbe whereas the term “genotype” is used torefer to the genetic constitution of a microbe.

The term “phenotype” may be used to refer to a collection of a microbe'sphysical and/or biochemical characteristics, which may optionally be thecollection of all of the microbe's physical and/or biochemicalcharacteristics; and/or to refer to one or more of a microbe's physicaland/or biochemical characteristics. For example, a microbe may bereferred to as having the phenotype of a particular microbial type,e.g., a Bacillus subtilis strain, and/or as having the phenotype ofbeing antibiotic-resistant.

The term “genotype” may be used to refer to genetic information, whichmay include genes, regulatory elements and/or junk DNA. The term“genotype” may be used to refer to a collection of a microbe's geneticinformation, which may optionally be the collection of all of themicrobe's genetic information; and/or to refer to one or more of amicrobe's genetic information. For example, a microbe may be referred toas having the genotype of a particular microbial type, e.g., a Bacillussubtilis strain, and/or as having the genotype of beingantibiotic-resistant.

The genotype of a microbe may or may not affect its phenotype, asexplained below.

The relationship between a genotype and a phenotype may bestraightforward. For example, if a microbe includes a functional geneencoding a particular protein, such as the adhesion protein FimHadhesin, then it will typically be phenotypically FimH adhesin-positive,i.e. have the FimH adhesin protein on its surface, whereas if a microbelacks a functional FimH adhesin gene, then it will have a FimHadhesin-negative phenotype.

A mutant genotype may result in a mutant phenotype. For example, if amutation destroys the function of a gene, then the loss of the functionof that gene may result in a mutant phenotype. However, factors such asgenetic redundancy may prevent a genotypic trait to result in acorresponding phenotypic trait. For example, a microbe may have multiplecopies of a particular gene, or have genes encoding alternative pathwaysthat lead to the same result.

It must also be borne in mind that many genotypic changes may have nophenotypic effect, e.g., because they are in junk DNA, i.e. DNA whichseems to serve no sequence-dependent purpose, or because they are silentmutations, i.e. mutations which do not change the coding information ofthe DNA because of the redundancy of the genetic code.

The phenotype of a microbe may be determined by its genotype in that acell requires genetic information to carry out cellular processes andany particular protein may only be generated within a cell if the cellcontains the relevant genetic information. However, the phenotype of amicrobe may also be affected by environmental factors and/or stresses,such as, temperature, nutrient and/or mineral availability, toxins andthe like. Such factors may influence how the genetic information isused, e.g., which genes are expressed and/or at which level.Environmental factors and/or stresses may also influence othercharacteristics of a microbe, e.g., heat may make membranes more fluid.

If a functional transgene is inserted into a cell at the correct genomicposition, then this may result in a corresponding phenotype.

The insertion of a transgene may affect a microbe's phenotype, but analtered phenotype may optionally only be observed under the appropriateenvironmental conditions. For example, the insertion of a transgeneencoding a protein involved in a synthesis of a particular substancewill only result in microbes that produce that substance if the microbesare provided with the required starting materials.

Optionally, the method may involve the analysis of the phenotype and/orgenotype of a microbial population.

The genotype and/or phenotype of a microbial population may bemanipulated, e.g., to analyse a cellular process, to make a microbialpopulation more suitable for drug screening and/or production, and thelike. Optionally, the method may involve the analysis of the effect ofsuch a genotype and/or phenotype manipulation on the microbialpopulation, e.g., on the genotype and/or phenotype of the microbialpopulation.

The method may optionally be used to analyse a microbial populationafter mutagenesis. Conventional methods for confirming whether or not amicrobe has been mutated can be difficult and/or time consuming.Optionally, the method may be used to analyse whether a microbe has beenmutated. A mutation may, e.g., be the introduction of a new gene, thesilencing of a gene, an alteration in the expression of a gene, or giverise to an altered protein. Silencing may, e.g., be achieved via geneknock-out.

Optionally, the method may be used to analyse the effect of mutagenesison a microbe, e.g., on the genotype and/or phenotype of a microbe.

Optionally, the method may analyse a microbe at 2 or more time points,e.g., before and after mutagenesis, and/or at 2 or more time pointsafter mutagenesis.

Optionally, the microbial population may be homogeneous orheterogeneous. By “homogeneous”, “homogeneity” and derivatives of theseterms is meant that the population is uniform, and by “heterogeneous”,“heterogeneity” and derivatives of these terms is meant that thepopulation is non-uniform.

By “degree of homogeneity” or “degree of heterogeneity” is meant theextent to which a microbial population is homogeneous or heterogeneous,which may be expressed as a percentage. For example, a microbialpopulation may be considered to have a high degree of homogeneity if atleast 60, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100%of the microbes are homogenous. A microbial population may be consideredto have a high degree of heterogeneity if at least 40, 45, 50, 55, 60,70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of themicrobes are heterogeneous.

The homogeneity and/or heterogeneity may be with respect to one or moregenotypic and/or phenotypic features, e.g., at least 1, 2, 3, 4, 5, 6,7, 8, 9 or 10 genotypic and/or phenotypic features, optionally withrespect to the microbes' entire genotype and/or phenotype.

Optionally, the method may involve the analysis of the degree ofhomogeneity and/or heterogeneity of the microbial population.

During culture of a microbial population, microbes may grow andreplicate. Replication may involve self-renewal, i.e. the production ofa daughter cell having the same genotype and/or phenotype as the mothercell, and/or differentiation, i.e. the production of a daughter cellhaving a different genotype and/or phenotype compared to the mothercell.

Alternatively or in addition, microbes may acquire one or more mutationsand thus acquire a different genotype, which may manifest itself as adifferent phenotype.

In a heterogeneous microbial population, one type may grow and/orreplicate better or in a different way to another microbial type, and/orone cell type may become dormant and/or die.

For these and/or other reasons, microbial population may become more orless heterogeneous, so the method may optionally involve monitoring forany changes in the homogeneity and/or heterogeneity of the microbialpopulation.

Optionally, if the degree of homogeneity and/or heterogeneity of themicrobial population is higher or lower than desired, the method mayinvolve a step of influencing the degree of homogeneity and/orheterogeneity. This may, e.g., involve the adjustment of cultureconditions and/or the addition of a substance, to affect, e.g., thegrowth and/or differentiation rate of one or more of the microbial typespresent in the microbial population.

Manipulation of Genotype and/or Phenotype

Optionally, a microbial population may be manipulated, e.g., thephenotype and/or genotype of some or all of the microbes that make upthe microbial population may be manipulated.

The manipulation may optionally involve the exposure of a microbialpopulation or a portion thereof to a compound and/or radiation.

The manipulation may optionally be genetic manipulation.

Genetic manipulation may alter one or more genomic region(s) of amicrobe, which genomic region may be in the coding region of a gene, thenon-coding region of a gene, a regulatory region, e.g., a promoter orenhancer, and/or in a region called “junk” DNA.

Genetic manipulation may optionally involve random mutagenesis. Forexample, microbes may be exposed to a mutagen, which may, e.g., beselected from a chemical mutagen and/or radiation.

A compound, which may optionally be a chemical mutagen, may optionallybe selected from, e.g., an alkylating agent, cross-linking agent, and/orpolycyclic aromatic hydrocarbons (PAHs). Alkylating agents act by addingmolecular components to DNA bases, which alters the protein product.Cross-linking agents create covalent bonds with DNA bases, while PAHsare metabolized by the human body into other potentially mutagenicmolecules.

Radiation may optionally be selected from, e.g., light of a suitablewavelength, heat, and/or ionizing radiation. Ionizing radiation canpenetrate microbes and create ions in the cell contents. These ions cancause permanent alterations in DNA. Ionizing radiation may optionally beselected from, e.g., x rays, gamma rays, neutrons, electrons (“beta”particles), and/or alpha particles (helium nuclei). Ionizing radiationcan alter the way two strands of DNA interact. It can rearrange entiresections of the chromosomes, altering relatively long stretches of DNA.Light may optionally be, e.g., UV light. This can cause covalent bondsto form between neighbouring thymine bases in the DNA, thereby alteringthe DNA at that location.

Alternatively or in addition to random mutagenesis, genetic manipulationmay optionally involve targeted mutagenesis, which may optionally, e.g.,be the knock-out, alteration, and/or insertion of genetic information. Acell that has been manipulated via targeted mutation may be referred toas a “transformed” cell, particularly if a new gene or gene variant,i.e. a “transgene” has been inserted. Similarly, a microbial populationcomprising or consisting of cells that have been manipulated viatargeted mutation may be referred to as a “transformed” microbialpopulation. Similarly, an organoid comprising or consisting of cellsthat have been manipulated via targeted mutation may be referred to as a“transformed” organoid.

Mutagenesis may optionally involve, e.g., one or more of the followingtechniques to introduce the desired genetic material, such as atransgene, into a cell: microinjection into the nucleus of a cell; aviral vector, e.g., an adenoviral or lentiviral vector; a liposome;calcium phosphate; a dendrimer; a cationic polymer, such as DEAE-dextranand/or polyethylenimine; sonication; electroporation; magnet-assistedtransfection with magnetic particles; and/or particle bombardment.

Mutagenesis may optionally involve genome editing, e.g., usingprogrammable nucleases, such as zinc-finger nucleases (ZFNs),transcription activator-like effector nucleases (TALENs), and/orclustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9 (Cas9).

Optionally, the method may involve a step of random and/or targetedmutagenesis, e.g., via any of the methods mentioned herein.

Analysis of Contamination

A microbial population may be at risk of contamination with anothermicrobe type. In particular, a microbial culture, such as a microbialtype culture, should be free of contamination. By “contamination” ismeant that the microbial culture is not pure, i.e. that one or morefurther (unwanted) microbial types are present.

Contamination can be difficult to detect with conventional methods,because two microbial types may be visually indistinguishable from oneanother.

Optionally, the method may be used to analyse whether contamination ispresent in a microbial population.

Optionally, the method may be used (i) to determine whether or not saidmicrobial population suffers from contamination; (ii) to determinewhether or not said microbial population is contamination free; (iii) todetermine whether or not said microbial population has been cured ofcontamination; (iv) to determine the progression or stage ofcontamination of a microbial population; or (v) to determine theprogression or stage of a treatment for contamination of a microbialpopulation.

Optionally, if contamination is determined, the contaminating microbemay be identified.

Optionally, if contamination is determined, the method may involve astep of treating/removing the contamination, e.g., through contactingthe microbial population with an appropriate substance that is effectiveat selectively killing, or inhibiting the growth of, the contaminationmicrobe. For example, if contamination with a bacterium is determined, asuitable antibiotic may be used.

Properties of Microbial Populations

The method may be carried out on a microbial population having a desiredproperty by selecting an existing microbial population with the desiredproperty. Alternatively or in addition, a microbial population may bemanipulated to impart a desired property unto the microbial population.The method may optionally involve the analysis of one or more propertiesof a microbial population.

Properties of a microbial population that may be selected, manipulated,analysed or the like may optionally be selected from any of theproperties listed below.

The microbial population may optionally be auxotrophic with respect toone or more substances. Auxotrophy is the inability, or reduced ability,of a microbe to synthesize a particular substance required for itsgrowth. An auxotroph is a microbe that displays this characteristic;auxotrophic is the corresponding adjective. For example, an auxotrophmay have a deficiency in a metabolic enzyme required to make tryptophan,in which case it may be referred to as a trp-auxotroph.

The microbial population may optionally have the ability to produce adesired substance, e.g., a drug. Thus, the microbial population mayoptionally have the ability to utilise a substance, e.g., to metabolisea substrate molecule to form a desired compound or precursor.Utilisation of a first substance may involve using a first substance asa substrate to produce a second substance; using a first substance as ageneral nutrient; and/or breaking down a first substance.

The microbial population may optionally have a high specificproductivity with respect to the production of a desired substance. Themicrobial population may optionally have a high efficiency with respectto the utilisation and/.or breakdown of a desired substance. It mayoptionally comprise high levels of, or have the ability to generate highlevels of, one or more key metabolites linked to energy generation,regulation of cellular redox potential, and precursors forglycosylation. The method may optionally be used to determine whether alow productivity/efficiency microbial population exhibits a differentmetabolic profile than its high productivity/efficiency counterpart. Themethod may optionally be used to analyse the specific productivitypotential and/or or efficiency with respect to the utilisation and/orbreakdown of a desired substance, of a microbial population.

The microbial population may optionally have the ability to secrete aproduced substance.

The microbial population may optionally have the ability to replicaterapidly.

The method may optionally be used to analyse the metabolome, lipidomeand/or proteome of a microbial population.

The metabolome is a collection of some or all of the small-moleculemetabolites present in a cell. The lipidome is a collection of some ofall of the lipids present in a microbe. The proteome is a collection ofsome of all of the proteins present in a microbe. Although many proteinsand some metabolites may not necessarily be analysed directly via themethod provided herein, they may optionally be analysed indirectly, byanalysing an indirect biomarker therefor.

The method may optionally involve the analysis of the state of amicrobial population or one or more microbe types present therein. By“state” is meant the condition of a microbial population or one or moremicrobe types present therein, which may, e.g., be healthy and growing;healthy and not growing; stressed and growing; stressed and not growing;dying; or dead.

The method may optionally involve the analysis of the viability of amicrobial population. By “viability” is meant the minimum length of timethat the microbial population will continue to live. The viability mayalso be referred to as the “robustness”, as robust microbial populationsare likely to live longer than non-robust microbial populations.

The method may optionally involve the analysis of a cellular process. Acellular process may, e.g., be the production of a substance; theutilisation of a nutrient; a response to exposure to a substance; aresponse to exposure to an environmental condition and the like.

The method may optionally involve the identification of a spectrometricbiomarker for a microbial type, phenotype, genotype and/or a microbialproperty. The identity and characteristics of many microbialpopulations, e.g., E. coli, are known, and the method provided hereinallows the identification of spectrometric biomarkers of these microbialtypes or microbial characteristics. Thus, the method may be used, e.g.,to correlate a characteristic with spectrometric data, e.g., aspectrometric biomarker. The characteristic may, e.g., be thesensitivity to a particular substance.

Drug Discovery and Screening of Agents, e.g., Antimicrobial Agents

It is known to use microbe-based platforms to advance drug discovery,and it will be understood by those skilled in the art that microbe-based compound screens and bioassays are essential for such drugdiscovery.

Optionally, the method provided herein may be used for drug discoveryand/or drug analysis. Thus, it may, e.g., be used as a screening methodto screen potential therapeutic agents; or to screen known therapeuticsto analyse their effects. For example, the method may be used to analysethe efficacy of a substance; the mechanism of action of a substance;and/or the safety of a substance. The efficacy may optionally be thetherapeutic efficacy. The safety may optionally be the pharmacologicalsafety.

Optionally the screening may be high-throughput screening. Optionally,the screening may be for, or of, a therapeutic agent effective againstany of the diseases listed elsewhere herein, e.g., an infection.

Thus, the method may optionally comprise exposing a microbial populationto a first substance and using the method to analyse the effect of saidsubstance on the microbial population. Details of suitable substancesare discussed elsewhere herein.

Optionally, a second substance may be used, e.g., for comparison orcontrol purposes. For example, the method may comprise exposing a firstmicrobial population to a first substance and a second microbialpopulation may be exposed to a second substance, analysing the first andthe second microbial population via mass spectrometry and/or ionmobility spectrometry as discussed elsewhere herein and analysing anydifferences between the two microbial populations. Optionally, thesecond substance may be a control substance, which may, e.g., be anegative control such as water or a buffer, or a positive control, suchas an agent with a known effect, e.g., a known antimicrobial effect.Optionally, the first and the second microbial population may beidentical prior to performance of the method. For example, two samplesmay be taken from a single microbial population to generate 2 microbialpopulations. Optionally, the first and the second microbial populationmay be isogenic. Optionally, the first and the second microbialpopulation may be phenotypically and/or genotypically different, e.g.,they may be different microbe types.

Analysing the effect of said substance on the microbial population maycomprise analysing a change in one or more properties of the microbialpopulation, details of which are discussed elsewhere herein.

Thus, optionally the method may comprise analysing said spectrometricdata in order to determine whether or not said microbial population hasinteracted with said substance in a manner which is of potentialinterest.

An “interaction in a manner which is of potential interest” is meantthat the interaction results in a phenotypic and/or genotypic change.Optionally, the phenotypic and/or genotypic change may be a change inone or more properties of the microbial population, details of which areprovided elsewhere herein.

Optionally, the EC₅₀ of a test substance may be tested. EC₅₀ is theconcentration of a drug that gives half-maximal response.

For example, the toxicity of a test substance may be tested, e.g., thepercentage of surviving microbes as a function of the concentration ofthe test substance may be measured and microbial populations which aresensitive and/or resistant to a test substance may be identified.

Optionally, the method may comprise analysing the susceptibility of amicrobial population to a substance, e.g., the susceptibility of amicrobial population to a known or potential therapeutic agent. Forexample, the susceptibility of a microbial population to ananti-microbial drug may be analysed.

Optionally, the method may also comprise a step of analysing the effectof an environmental condition of the microbial population on theresponse of a microbial population to said substance. Details ofenvironmental conditions of the microbial population are providedelsewhere herein. Thus, said method may optionally comprise the steps of(i) exposing the microbial population to a substance; and (ii) changingan environmental condition of the microbial population.

Optionally, the steps of (i) exposing the microbial population to asubstance; and (ii) changing an environmental condition of the microbialpopulation may be carried out simultaneously or sequentially in anyorder. Any of these steps, alone and/or in combination, may optionallybe repeated, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 times.

Thus, a microbial population may optionally, e.g., be exposed to asubstance, and an environmental condition of the microbial populationmay subsequently be changed; an environmental condition of the microbialpopulation may be changed, and a microbial population may subsequentlybe exposed to a substance; and/or a microbial population may be exposedto a substance, and an environmental condition of the microbialpopulation may simultaneously be changed.

It will be understood that optionally, one or more different substancesand/or one or more different environmental conditions may be used in anyof these methods. For example, a panel of different substances mayoptionally be used. The panel may, e.g., comprise or consist of membersof a single class of drugs and/or members of two or more classes ofdrugs, e.g., known and unknown drugs.

For example, optionally, the substance may be an antimicrobial drug.Thus, e.g., one or more microbial populations may be tested with one ormore known antimicrobial drugs. Optionally, one or more microbialpopulations may, e.g., be tested using one or more potentially newtherapeutic agents or antimicrobial drugs.

Optionally, one or more microbial populations may be geneticallymodified and the modified microbial population may be tested, e.g., witha known cytotoxic/cytostatic drug, and/or against a panel of potentiallynew therapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more microbial populations may be tested against afirst substance and subsequently be tested against a second substanceand optionally one or more further substances.

Analysis of a Change

The optional analysis of a change may be carried out in one or moredifferent ways.

Optionally, a microbial population may be analysed via the methodprovided herein at a first time and at a subsequent further time, e.g.,second time, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth,etc. time.

Thus, optionally, the method may comprise generating said aerosol, smokeor vapour from said target at a first time so as to obtain said firstspectrometric data;

generating aerosol, smoke or vapour from said target, at a subsequenttime;

mass analysing and/or ion mobility analysing the aerosol, smoke orvapour generated at the subsequent time, or ions derived therefrom, soas to obtain second spectrometric data; and

comparing the first and subsequent spectrometric data to determinechanges in the target. The subsequent time may be a second time, third,fourth, fifth, sixth, seventh, eighth, ninth, tenth, etc. time.

Optionally, between the first and a subsequent time, the microbialpopulation may be manipulated and/or exposed to a substance, which mayoptionally be selected from any of the agents listed herein, such as, atest agent.

Optionally, 2 or more, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700,800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000identical and/or non-identical microbial populations may be analysedsimultaneously and/or sequentially. If a group of 3 or more microbialpopulations are analysed, then the group may optionally comprise, e.g.,2 or more microbial populations that are identical to one another, aswell as 2 or more microbial populations that are non-identical to oneanother.

Optionally, one or more further test agents and/or reference or controlagents may be used. For example, a first microbial population may beexposed to a first test agent and a second microbial population may beexposed to a further test agent, a reference agent or a control agent.

Environmental Conditions

The method allows the analysis of a microbial population under a definedenvironmental condition. This may optionally allow, e.g., microbialpopulations to be analysed under conditions that mimic in vivoconditions, e.g., the conditions of a host microenvironment.

By “defined environmental condition” is meant that at least oneenvironmental factor is controlled. For example, a controlledtemperature or temperature range, or a controlled level of a particularnutrient, may be referred to as a defined environmental condition.

Optionally, the method may involve the analysis of the effect of one ormore defined environmental conditions on a microbial population.Optionally, the analysis may be of the effect of a change in one or moreenvironmental conditions on a microbial population.

The environmental condition may optionally be a condition that caninfluence microbial population growth; differentiation; migration;microbe state; and/or phenotype and/or genotype. Thus, the environmentalcondition may, e.g., be the nature and/or concentration of culture mediacomponents, particularly nutrient and/or mineral concentrations; thenature and extent of cell-cell contacts; temperature; pH; fluid balance;pressure; flow volume; and/or oxygen pressure. For example, themicrobial population may be exposed to hypoxia.

The environmental condition may optionally be altered by introducing themicrobial population into a host organism or a specimen thereof. Thus,optionally, the microbial population may be introduced into a hostorganism or specimen thereof. The host organism may optionally beselected from a human or non-human animal. Optionally, it may be alivestock, domestic or laboratory animal, e.g., be a rodent. Optionally,it may be murine, guinea pig, hamster, rat, goat, pig, cat, dog, sheep,rabbit, cow, horse, alpaca, ferret, fowl, buffalo, and/or monkey. Thus,optionally the cell population may be exposed to, e.g., maintainedand/or grown in, the in vivo environment of a host organism or the or exvivo environment of a host organism specimen. The effect of such anexposure may optionally be analysed by the method provided herein.

Prior to and/or after analysis of a target, one or more of theseconditions may optionally be appropriately modified. Such modificationis within the competencies of one of ordinary skill in the art.

Thus, optionally the method may comprise one or more of the following:changing or varying the concentration of a nutrient which is supplied toa microbial population; changing or varying the concentration of amineral which is supplied to a microbial population; changing or varyinga pH level at which said microbial population is maintained; changing orvarying a temperature at which said microbial population is maintained;changing or varying an oxygen, carbon dioxide or other gas level towhich said microbial population is exposed; changing or varying theconcentration of a contamination control substance or an antibiotic towhich said microbial population is exposed; changing or varying theconcentration of a catalyst, inducer or agent which prompts saidmicrobial population to generate a therapeutic or other product; and/orchanging or varying a light level to which said microbial population isexposed. Optionally, the effect of any of these changes may be analysed.

For example, the environmental condition may be a culture medium whichhas a low concentration of one or more lipids, and/or a low overalllipid concentration.

Isotope Studies

Optionally, the method may be used in or with isotope studies.

Isotopes are variants of a particular chemical element which differ inneutron number, whilst having the same number of protons in each atom.Isotope studies may, e.g., involve the use of stable isotopes, i.e.non-radioactive isotopes. For example, isotopes of hydrogen (H), carbon(C), nitrogen (N), oxygen (O), fluorine (F) and/or sulphur (S) may beused. The term “different types of isotopes” is used to mean isotopes ofdifferent elements, so an isotope of C is a different type of isotopefrom an isotope of N.

In nature, one isotope of each element is typically most abundant, andany other stable isotopes of the element that may exist are typicallyfar less abundant. For example, ₁H is far more abundant than ₂H, ₁₂C isfar more abundant than ₁₃C, ₁₄N is far more abundant than ₁₅N, ₁₆O isfar more abundant than ₁₇O or ₁₈O, and ₃₂S is far more abundant than₃₄S. The less abundant isotopes are the heavier ones, so they may bereferred to as a “heavy isotope”. In isotope studies, cells may beexposed to one or more heavy isotopes and cellular processes may then beanalysed by analysing the fate of the heavy isotope(s).

Different nonradioactive stable isotopes can be distinguished by massspectrometry or ion mobility spectrometry, so the method provided hereinmay optionally be used in or with isotope studies.

Thus, optionally, the microbial population may be exposed to one or moreheavy isotopes, e.g., to one or more substances comprising or consistingof one or more heavy isotopes. The substance may optionally be selectedfrom any of the substances listed elsewhere herein, e.g., any nutrients,e.g., glucose, glutamine o the like. A substance comprising orconsisting of one or more heavy isotopes may be referred to as a“heavy-isotope substance”. A heavy-isotope substance may optionallycomprise a single heavy-isotope, 2 or more heavy-isotopes, or consist ofheavy-isotopes. A heavy-isotope substance may optionally comprise asingle type of heavy isotope or 2 or more, e.g., at least 2, 3, 4, 5, or6 different types of heavy isotopes.

A substance may optionally be isotopically defined, i.e. it may bepossible to use a substance in which one or more specific atoms arereplaced with one or more heavy isotopes, which may allow an analysis ofthe fate of specific parts of a substance. Optionally, an analysis witha substance having a first atom replaced with a heavy isotope may becompared to an analysis with the corresponding substance having adifferent atom replaced with a heavy isotope.

For example, a heavy-isotope substance, such as, a nutrient, e.g.,carbon source, may be used and the method may optionally be used toanalyse whether and/or how the nutrient is used by the microbialpopulation. Thus, optionally, lipid, carbon and/or protein metabolism,e.g., anabolism and/or catabolism may be analysed. In particular, themethod may optionally be used to analyse the depletion or enrichment ofa heavy isotope type in one or more metabolites, such as, fatty acidtype(s). Thus, e.g., the presence or absence, and/or relative abundance,of one or more metabolites, fatty acids, lipids and/or biomarkers may beanalysed prior to and/or after exposure of a microbial population to aheavy isotope. Optionally, the analysis may be carried out at 2 or moretime points, e.g., to monitor a change over time in the presence orabsence, and/or relative abundance, of one or more metabolites, fattyacids, lipids and/or biomarkers.

Optionally, a microbial population may be exposed to at least 2 types ofheavy isotopes and/or at least 2 types of heavy isotope substancessimultaneously and/or sequentially. Optionally, a microbial populationmay be exposed to a first type of heavy isotope at a first time pointand to a second type of heavy isotope at a second time point.Optionally, a microbial population may be exposed to a first type ofheavy isotope substance at a first time point and to a second type ofheavy isotope substance at a second time point. For example, a microbialpopulation may be exposed to a heavy-isotope glucose at a first timepoint and to a heavy-isotope glutamine at a second time point.

Isogenic Microbial Populations

Optionally, the method may involve the use of 2 or more microbialpopulations that are isogenic except for one or more genetic regions ofinterest. The term “isogenic” is used in the art to indicate that 2microbial populations are genetically identical or share essentially thesame genetic information, except for one or more genetic regions ofinterest. Typically, 2 isogenic microbial populations will differ in asingle gene, which may optionally be linked to a reporter gene in whichthe isogenic microbial populations may also differ.

Optionally, isogenic microbial populations may differ with respect to anendogenous gene, e.g., one microbial population may have a wild-typeendogenous gene and another microbial population may have a mutantversion of said gene. Optionally, the mutant version may have an alteredfunctionality or be a knock-out.

Optionally, isogenic microbial populations may differ with respect to anexogenous gene, e.g., one microbial population may comprise a firstversion of an exogenous gene and another microbial population may have asecond version of said exogenous gene; or one microbial population maycomprise a first exogenous gene and another microbial population mayhave a second exogenous gene.

The method may thus optionally be used to analyse differences between 2or more isogenic microbial populations. The use of isogenic microbialpopulations may be useful, e.g., to analyse the effect of a modificationor change, e.g., to analyse the effect of a substance on a microbialpopulation; to analyse the effect of an environmental change on amicrobial population; and/or to analyse the production of a substance bya microbial population.

Isogenic microbial populations may be obtained, e.g., by transfecting afirst microbial population with a first vector that encodes a firsttransgene and a first marker and transfecting a second microbialpopulation with a second vector that encodes a second transgene and asecond marker, the second being different from the first.

Optionally, the marker may, e.g., be a fluorescent marker, details ofwhich are provided elsewhere herein.

Culture and analysis via the method provided herein of both microbialpopulation allows, e.g., screening for compounds with selectiveactivity, e.g., toxicity, towards a gene of interest. Such drugscreening is broadly applicable for mining therapeutic agents targetedto specific genetic alterations responsible for disease development.

Substance Production and/or Utilisation by Microbial Populations

Microbes may produce substances, such as quorum sensing molecules,virulence factors and the like, so the analysis of the production ofsubstances by a microbial population may provide useful informationregarding the identity and/or characteristics of the microbialpopulation, such as, their virulence, their interaction with theirenvironment and the like. Details of suitable substances are providedelsewhere herein.

Microbial populations may be used for the production of substances, suchas, therapeutics, fuel, food etc. For example, the substance may be abiopharmaceutical, e.g., antibody, hormone and/or cytokine. Microbialpopulations may utilise a first substance as a substrate to produce asecond substance. Microbial populations may be used to break downsubstances, optionally into useful and/or less harmful substances. Theymay, e.g., be used to break down industrial waste products, pollutants,herbicides, pesticides, explosives, and the like.

The method may therefore optionally be used to analyse the ability of amicrobial population to utilise and/or produce a substance, and/or toanalyse the utilisation and/or production of a substance by a microbialpopulation. Details of suitable substances are provided elsewhereherein.

Optionally, the method may involve the purification of a substance, soit may include a step of purifying a substance. A step of purifying thesubstance may, e.g., comprise one or more of lysis of cells;centrifugation, e.g., to achieve isopycnic banding and/ornon-equilibrium settling; filtration; membrane separation, which may,e.g., be microfiltration, ultrafiltration, and/or dialysis; extraction,which may, e.g., be fluid extraction, and/or liquid/liquid extraction;precipitation, which may, e.g., be fractional precipitation;chromatography, which may, e.g., be ion-exchange chromatography, gelfiltration chromatography, affinity chromatography, hydrophobicinteraction chromatography, high performance liquid chromatography(“HPLC”), and/or adsorption chromatography. Optionally, it may involveprecipitation of a free acid form of said substance, and, optionally,conversion of a free acid form of said substance to a salt of saidcompound.

Microbial populations may be used for the breakdown of substances,optionally into useful and/or less harmful substances. They may, e.g.,be used to break down industrial waste products, pollutants, herbicides,pesticides, explosives, and the like. Details of suitable substances areprovided elsewhere herein.

Identification of Utilisation/Production/Breakdown Microbial Populations

Optionally, the method may be used as a screening method, e.g., toidentify a suitable utilisation/production/breakdown microbialpopulation, and/or to distinguish between microbial populations withdifferent utilisation/production/breakdown properties.

Cells may optionally be manipulated, e.g., genetically manipulated, togenerate a microbial population having one or more desired properties.Optionally, the method may involve an analysis to identity/select amicrobial population which has successfully been manipulated, e.g., toidentify a microbial population having the desired genotype and/orphenotype.

Conventional methods for deriving a suitableutilisation/production/breakdown microbial population, e.g., ahigh-turnover population from parental population, may be quitetime-consuming and laborious and may, e.g., take more than six months inindustrial settings. The first step may be genetic manipulation, whichis exemplified in the discussion below by the insertion of a transgene.Once the transgene enters the microbe, the integration site of the genemay be random, and expression of the transgene may, in part, be dictatedby the surrounding genetic structure. High expression of the transgenemay be very desirable. Optionally, the method provided herein may beused to speed up the process of identifying/selecting a suitableproduction/breakdown microbial population.

A large number, e.g., a pool of at least or about 50,000, 40,000,30,000, 20,000, 10,000, 5000, 1000 or 500 microbial populations may bescreened to select a small number, e.g., about or no more than 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 80, 100 or 200 candidatepopulations.

Optionally, one or more strategies may be used to improve the generationand/or selection of microbes that have a transgene integrated at atranscriptionally active site. For example, the transgene construct mayoptionally include one or more antibiotic resistance gene(s). If anantibiotic resistance gene is used, then, following mutagenesis,microbes stably expressing the transgene construct and hence theantibiotic resistance factor, may optionally be selected using therelevant antibiotic.

A selection strategy, e.g., one of the ones mentioned herein, may yielda heterogeneous population of microbes having different transgeneconstruct integration sites, copy numbers and the like.

Optionally, a series of limiting dilutions, e.g., in multi-well plates,may be carried out to isolate uniform microbial populations, which mayoptionally be screened to select candidate microbial populations.

Optionally, candidate microbial populations may be evaluated in moredetail and/or on a larger scale to select one or more final candidates.

Optionally, the method provided herein may be used to analyse microbesat any stage of such a process of deriving a suitableutilisation/production microbial population. Optionally, expression ofthe transgene and/or one or more factors that influence the growth,efficiency and/or productivity of a cell may be analysed. This allowsthe rapid selection of a small number of candidates. For example, thepool of cells may be reduced by a factor of about or at least 10, 20,30, 40, 50, 60, 70, 80, 90, 100, 200, 500 or 1000.

Thus, optionally, the method may involve analysing a plurality ofmicrobial cultures, each microbial culture comprising one or moremicrobial populations. This may optionally involve generating aplurality of spectrometric data and determining from said plurality ofspectrometric data a first subset of microbial cultures which are ofpotential interest for utilising a substance and/or producing/breakingdown a substance of interest.

The method may optionally further involve the use of liquidchromatography based analysis, e.g., liquid chromatography massspectrometry (“LCMS”) analysis; liquid chromatography ion mobilityspectrometry (“LCIMS”) analysis; liquid chromatography tandem massspectrometry (“LCMS/MS”) analysis; liquid chromatography followed byMS^(E) spectrometry (“LCMS^(E)”) analysis; liquid chromatographyfollowed by ion mobility separation and then mass spectrometry(“LC-IMS-MS”) analysis; and/or liquid chromatography followed by ionmobility separation and then MS^(E) spectrometry (“LC-IMS-MS^(E)”)analysis. Such a liquid chromatography based analysis may optionally beused to analyse said first subset of microbial cultures, e.g., togenerate a second subset of microbial cultures. Optionally, the methodmay comprise classifying or dividing cell populations into subsets basedon spectrometric data, liquid chromatography based analysis data, or acombination of spectrometric data and liquid chromatography basedanalysis data.

However, the ambient ionisation mass spectrometry and/or ion mobilityspectrometry methods mentioned herein are much faster than liquidchromatography based analysis, so, optionally, the method optionallydoes not involve the use of a liquid chromatography based analysis,e.g., any of the ones listed above.

Thus, optionally, the method may be used in the process of drugmanufacture and production.

Conventionally, a very large number (e.g., approximately 50,000) ofpotential batches of a microbial culture may be produced. Liquidchromatography analysis, e.g., LCMS may then be performed on each of themicrobial cultures in order to determine a small subset of microbialcultures which are of greatest interest in terms of taking on into fullproduction.

However, it will be appreciated that subjecting approximately 50,000separate batches of microbial populations to LCMS analysis is a complexand time consuming process.

One particular advantage of the method provided herein is that themethod provided herein enables experimental results to be produced onessentially an instantaneous basis. Furthermore, the method providedherein lends itself to automation and a large number of microbialcultures can be analysed either in sequence and/or in parallel in acomparatively short period of time. Certainly, it is possible to analyseapproximately 50,000 separate batches of microbial cultures on atimescale which is several orders of magnitude faster than conventionalLCMS approaches.

Accordingly, one particular application of REIMS and related ionisationtechniques is the ability to analyse a large number of microbialcultures in a short period of time.

This analysis enables the large number of microbial cultures, e.g.,50,000, to be reduced to a very small candidate list of, for example,just ten microbial cultures which can then be taken on to fullproduction/utilisation.

Alternatively, other embodiments are contemplated wherein REIMS analysismay be performed on the approximately 50,000 batches enabling a firstsubset of microbial cultures to be established. The first subset ofmicrobial cultures may comprise, for example, approximately 1000batches. Liquid chromatography analysis, e.g., LCMS can then beperformed on this reduced number of 1000 samples in order to determine asecond subset of microbial cultures (e.g., 10) which are the mostpromising to be taken on to full production. Although this alternativeapproach still involves using liquid chromatography analysis, e.g.,LCMS, the two-stage process still results in considerable time savingssince only e.g., 1000 batches need to be analysed by liquidchromatography analysis, e.g., LCMS (c.f. approximately 50,000 as perthe conventional approach).

Determining or establishing one or more subsets may optionally involve aclassification and/or a physical separation.

Substance Utilisation/Production/Breakdown and Quality Control

A general process of utilising, producing and/or breaking downsubstance, such as a (bio)therapeutic product, via microbial culture mayinclude one or more of the following steps:

-   1. Set up microbial culture apparatus with suitable microbial    culture conditions;-   2. Inoculate with microbial population, e.g., starter culture grown    on smaller scale;-   3. Allow microbes to grow-   4. If utilisation/production/breakdown of substance is not automatic    (e.g., if dependent on a particular temperature or nutrient), adjust    conditions to induce utilisation/production/breakdown;-   5. Monitor culture conditions and adjust as required;-   6. Monitor substance utilisation/production/breakdown; 7. Harvest    substance

from culture medium, if substance or breakdown product is secreted

from microbes, if substance or breakdown product accumulates withinmicrobes

-   8. Purify substance, e.g., by removing any contaminants

During a process of utilising and/or producing a substance via microbialculture, analysis may be carried out, e.g., via the method describedherein described herein, e.g., for monitoring the culture conditionsand/or substance utilisation/production. This may optionally involveobtaining a sample from the microbial population for analysis. Theskilled person will be aware of suitable sample acquisition methods,such as pipetting, using a swab or the like. The sample may optionallybe processed, e.g., a liquid sample may be filtered or processed togenerate a pellet as mentioned elsewhere herein. Optionally, a swab maybe used, which may optionally be analysed without further processingusing the method with a REIMS ion source.

Optionally, any adjustments to the culture conditions may be made, e.g.,if the analysis reveals the necessity for an adjustment. The culture pHmay be measured, e.g., with a pH meter, and optionally adjusted, e.g.,by adding an acid or a base as required. Nutrient use may be monitoredby analysing, e.g., respiration. The Respiratory Quotient, i.e. theratio of the Carbon Dioxide Evolution Rate to the Oxygen Uptake Rate,may be analysed. Metabolic products, e.g., the substance of interest, abreakdown product and/or contaminants, may be analysed.

Thus, optionally, the method may involve analysing, e.g., monitoring, aprocess of utilising/producing/breaking down a substance via culture ofa microbial population. Optionally, the invention provides a method ofutilising/producing/breaking down a substance via culture of a microbialpopulation, wherein said method includes a step of analysing theutilisation/production/breakdown process via a method of analysis of theinvention. Optionally, said method further comprises a step of adjustingthe culture conditions on the basis of the analysis.

Thus, the method may optionally comprise analysing the utilisation,production and/or breakdown of a substance by a microbial population.There is provided a method of producing and/or breaking down asubstance, comprising (a) using a first device to generate smoke,aerosol or vapour from a target in vitro or ex vivo microbialpopulation; (b) mass and/or ion mobility analysing said smoke, aerosolor vapour, or ions derived therefrom, in order to obtain spectrometricdata; and (c) analysing said spectrometric data in order to analyse theproduction and/or break down of a substance by said target microbialpopulation. Also provided is a method of identifying a microbialpopulation capable of utilising, producing and/or breaking down asubstance, comprising (a) using a first device to generate an smoke,aerosol or vapour from a target in vitro or ex vivo microbialpopulation; (b) mass and/or ion mobility analysing said smoke, aerosolor vapour, or ions derived therefrom, in order to obtain spectrometricdata; and (c) analysing said spectrometric data in order to identify amicrobial population capable of utilising, producing and/or breakingdown a substance.

Any of these methods may optionally comprise analysing saidspectrometric data in order to (i) determine whether said microbialpopulation utilises, produces and/or breaks down said substance; (ii)determine the rate at which said microbial population utilises, producesand/or breaks down said substance; (iii) determine whether saidmicrobial population produces any by-products; and/or (v) determine themechanism by which said microbial population utilises, produces and/orbreaks down said substance. Optionally, two or more microbialpopulations may be analysed in order to determine which microbialpopulation utilises, produces and/or breaks down said substance at ahigher rate and/or at a higher purity. Optionally, a plurality ofmicrobial populations may be analysed and divided into 2 or more subsetsbased on said analysis. For example, microbial populations may bedivided based on said analysis into subsets based on (i) their abilityor inability to utilise, produce and/or break down said substance; (ii)the rate of utilisation, production and/or breakdown of said substance;(iii) the production and/or breakdown of any by-products; and/or (iv)the mechanism of utilisation, production and/or breakdown. Optionally,based on the analysis microbial populations may be divided into (i) afirst subset capable of utilising, producing and/or breaking down saidsubstance and a second subset incapable of utilising, producing and/orbreaking down said substance; (ii) a first subset and a second subset,wherein said first subset utilises, produces and/or breaks down thesubstance at a higher rate compared to the second subset; (iii) a firstsubset and a second subset, wherein said first subset produces noby-products, or fewer by-products compared to the second subset; and/or(iv) a first subset and a second subset, wherein said first subsetutilises, produces and/or breaks down the substance via a differentmechanism compared to the second subset.

Optionally, the method may further comprise a step of subjection themicrobial population or microbial population subset to liquidchromatography mass spectrometry (“LCMS”) analysis prior to and/or afterthe method of analysis. Optionally, based on said LCMS analysis,microbial population may be divided into subsets, or a said microbialpopulation subset as mentioned above may be divided into furthersubsets.

Optionally, the microbial population or microbial population subset maybe cultured under conditions suitable to utilise, produce and/or breakdown said substance.

Optionally, the method does not comprise a step of subjection amicrobial population, or microbial population subset, to liquidchromatography mass spectrometry (“LCMS”) analysis.

Optionally, the method may be used to monitor the utilisation and/orproduction of a substance, particularly to monitor the production ofby-products. This may involve analysing a sample from a microbialpopulation at various time points, as discussed elsewhere herein.

Click Chemistry

“Click Chemistry” is a term that was introduced by K. B. Sharpless in2001 to describe reactions that are high yielding, wide in scope, createonly by-products that can be removed without chromatography, arestereospecific, simple to perform, and can be conducted in easilyremovable or benign solvents.

A typical click chemistry (click reaction) is the copper-catalyzed1,3-dipolar cycloadditions between azides and acetylenes.

A click reaction may, e.g., happen between a fluorescent probecomprising an alkyne and a biomolecule comprising an azide.

Thus, click chemistry may be used for attaching a probe or substrate ofinterest to a specific biomolecule, a process called bioconjugation. Thepossibility of attaching fluorophores and other reporter molecules hasmade click chemistry a very powerful tool for identifying, locating andcharacterizing both old and new biomolecules.

One of the earliest and most important methods in bioconjugation was toexpress a reporter on the same open reading frame as a biomolecule ofinterest. Notably, green fluorescent protein (“GFP”) is expressed inthis way at the N- or C-terminus of many proteins. However, thisapproach comes with several difficulties. For instance, GFP is a verylarge unit and can often affect the folding of the protein of interest.Moreover, by being expressed at either terminus, the GFP adduct can alsoaffect the targeting and expression of the desired protein. Finally,using this method, GFP can only be attached to proteins, and notpost-translationally, leaving other important biomolecular classes(nucleic acids, lipids, carbohydrates, etc.) out of reach.

To overcome these challenges, chemists have opted to proceed byidentifying pairs of bioorthogonal reaction partners, thus allowing theuse of small exogenous molecules as biomolecular probes. A fluorophorecan be attached to one of these probes to give a fluorescence signalupon binding of the reporter molecule to the target—just as GFPfluoresces when it is expressed with the target.

Optionally, the method provided herein may involve monitoring a clickchemistry reaction, e.g., to detect the end-products and/or anyby-products of a click chemistry reaction. Optionally, the method may beused in combination with click chemistry, e.g., before or after a clickchemistry reaction. Optionally, the method may be used instead of clickchemistry. For example, the method may allow the analysis of biomarkersthat would conventionally be analysed by using click chemistry, thusobviating the need for a click chemistry reaction.

Diseases

The analysis may optionally relate to a disease or condition, such asany of the diseases or conditions listed in this section and/orelsewhere herein. The terms “disease” and “condition” are usedinterchangeably herein. For example, the target may be a subject havinga disease, or a specimen derived from such a subject.

The disease may optionally be, or associated with, injury, infection,cancer, infarction, toxins, inflammation, lack of proper care to a woundsite, frostbite, diabetes, and/or arteriosclerosis. The disease mayoptionally be an autoimmune disorder, an inflammatory disease, tropicalsprue, and/or a food intolerance.

Optionally, it may be an infection of any of the tissues mentionedelsewhere herein, e.g., a vaginal, lung, respiratory tract, brain, skinand/or gastrointestinal infection.

Optionally, it may be thrush, malaria, measles, meningitis, diarrhoea,Bronchitis, pharyngitis, laryngitis, Chronic obstructive pulmonarydisease (COPD), Pneumonia, sepsis, and/or Cystic fibrosis.

The disease may optionally be a cancer or tumour, which may optionallybe selected from, for example, carcinomas, sarcomas, leukaemias,lymphomas and gliomas. The disease may optionally be necrosis, which mayoptionally be, for example, coagulative, liquefactive, caseous, fatnecrosis, fibrinoid necrosis and/or gangrenous necrosis.

More particularly, the disease may optionally be selected from, forexample, asthma, Coeliac disease, gastritis, peptic duodenitis,Gluten-sensitive enteropathy; allergy and/or intolerance to an allergen,e.g., to milk, soy, tree nut(s), egg, wheat, meat, fish, shellfish,peanut, seed, such as sesame, sunflower, and/or poppy seeds, garlic,mustard, coriander, and/or onion; Hashimoto's thyroiditis; Irritablebowel syndrome; Graves's disease; reactive arthritis; psoriasis;multiple sclerosis; Systemic lupus erythematosus (SLE or lupus);ankylosing spondylitis; progressive systemic sclerosis (PSS);glomerulonephritis; autoimmune enteropathy; IgA deficiency; commonvariable immunodeficiency; Crohn's disease; colitis, such as,lymphocytic colitis, collagenous colitis and/or ulcerative colitis;diffuse lymphocytic gastroenteritis; ulcer; intestinal T-cell lymphoma.

Optionally, one or more of the microbes in the microbial population maybe (i) the cause of the disease; (ii) associated with the disease;and/or (iii) aggravating the disease.

Diagnosis and/or Treatment

Optionally, the method may be a method of treatment. Thus, the methodmay optionally comprise a step of administering a therapeuticallyeffective amount of a therapeutic agent to a subject in need thereof.

Optionally, the method may comprise obtaining spectrometric data from atarget in or from a subject as described elsewhere herein and analysingthe spectrometric data in order to assess the effectiveness of asubstance, optionally a therapeutic or test substance. Optionally, themethod may further comprise a step of determining whether the subjectshould receive a treatment. Optionally, the method may further comprisea step of treating the subject. The treatment may optionally be with ananti-microbial agent, e.g., any of the antimicrobial agents listedelsewhere herein, and/or with any of the compounds listed elsewhereherein.

Optionally, the method may be a method of diagnosis. Thus, the methodmay optionally comprise a step of making a diagnosis based on theanalysis of said spectrometric data.

Optionally, the method may include a step of diagnosis and a step oftreatment.

As mentioned elsewhere herein, the method involves the analysis of atarget entity, which may be a microbe and/or a compound. In the contextof diagnosis and/or treatment, the target entity may, e.g., be apathogenic microbe and/or a virulence factor.

The terms “diagnosis” or “diagnosing” and derivations of these terms asused herein refer to the determination whether or not a subject issuffering from a disease. Optionally, the method may involve analysing atarget and, on the basis of one or more of the following making adiagnosis that a subject is or is not suffering from a particulardisease: detecting a target entity; identifying a target entity;detecting an increase in a target entity; detecting a decrease in atarget entity.

An increase or decrease may be determined by reference to a suitablereference, comparator or control.

The term “monitoring” and derivations of this term as used herein referto the determination whether any changes take place/have taken place.Typically, it is determined whether any changes have taken place overtime, i.e. since a previous time point. The change may, for example, bethe development and/or progression of a disease, such as, any of thediseases mentioned. Optionally, the method may involve analysing atarget and, on the basis of one or more of the following monitoring asubject or disease: detecting a target entity; identifying a targetentity; detecting an increase in a target entity; detecting a decreasein a target entity.

The term “prognosis” and derivations of this term as used herein referto risk prediction of the severity of disease or of the probable courseand clinical outcome associated with a disease. Thus, the term “methodof prognosis” as used herein refers to methods by which the skilledperson can estimate and/or determine a probability that a given outcomewill occur. The outcome to which the prognosis relates may be morbidityand/or mortality. In particular, the prognosis may relate to“progression-free survival” (PFS), which is the length of time that asubject lives with the disease without the disease progressing. Thus,PFS may, for example, be the time from the start of therapy to the dateof disease progression, or the time from the end of therapy to the dateof disease progression.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following making a prognosis: detecting a targetentity; identifying a target entity; detecting an increase in a targetentity; detecting a decrease in a target entity.

By “progressing” or “progression” and derivations of these terms ismeant that the disease gets worse, i.e. that the severity increases. Forexample, in the case of a pathogenic infection, it may mean that thepathogen burden increases, for example a pathogen multiplies and/oracquires resistance to one or more antimicrobials.

The prognosis may relate to overall survival. By “overall survival” (OS)is meant the length of time that a subject lives with the disease beforedeath occurs. Overall survival may, for example, be defined as the timefrom diagnosis of the disease; the time of treatment start; or the timeof treatment completion, until death. Overall survival is typicallyexpressed as an “overall survival rate”, which is the percentage ofpeople in a study or treatment group who are still alive for a certainperiod of time after they were diagnosed with, or started treatment for,or completed treatment for, a disease. The overall survival rate may,for example, be stated as a one-year survival rate, which is thepercentage of people in a study or treatment group who are alive oneyear after their diagnosis or the start or completion of treatment.

Statistical information regarding the average (e.g., median, mean ormode) OS and PFS of subjects having a particular type of disease isavailable to those skilled in the art. A determination whether a subjecthas, or is likely to have, an increased or decreased OS or PFS comparedto such an average may therefore be made.

A determination that the likelihood and/or length of PFS and/or overallsurvival is decreased means that the prognosis is poor or adverse. Theterms “poor” and “adverse” are used interchangeably herein. A “poor”prognosis may be defined as a prognosis that is worse than the referenceprognosis for a subject, so it may also be referred to as a “worse”prognosis, and a “good” or “non-adverse” prognosis may be defined as aprognosis that is better than the reference prognosis for a subject soit may also be referred to as a “better” prognosis. The skilled personwill appreciate that for the “reference prognosis” subjects having thesame type of disease, optionally the same stage of disease, should beused. The “reference prognosis” may be the average prognosis or atypical prognosis determined by any other suitable method.

An adverse or worse prognosis may be defined as a shorter overallsurvival or an increased likelihood of shorter overall survival and/orshorter PFS or an increased likelihood of shorter PFS.

By “regressing” or “regression” is meant that the disease improves, i.e.that the severity decreases. For example, in the case of an infection,it may mean that the pathogen burden decreases.

By “development” is meant the onset of a disease.

The term “prediction” or “predicting” as used herein refers todetermining the likelihood of a particular outcome.

The term “stratification” or “stratifying” as used herein refers to thedivision of a population into subpopulations on the basis of specifiedcriteria. More particularly, it refers to the division of a cohort ofsubjects into at least two groups on the basis of specific criteria,which in the context of the present invention comprise or consist of theresults of the method of analysis. Optionally, subjects may bestratified into those likely to respond to a particular treatment andthose unlikely to respond; and/or subjects may be stratified based ontheir diagnosis, prognosis and/or the response that they have presentedto treatment.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, stratifying subjects: detecting atarget entity; identifying a target entity; detecting an increase in atarget entity; detecting a decrease in a target entity.

The term “treatment” or “treating” as used herein refers to a course ofaction which is aimed at bringing about a medical benefit for a subject.The treatment may be prophylactic or therapeutic.

By “prophylactic” is meant that the treatment is preventative, i.e. itis applied before the onset of disease. By “therapeutic” is meant thatthe treatment is applied after the onset of disease.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, determining that a subject should orshould not receive a particular treatment: detecting a target entity;identifying a target entity; detecting an increase in a target entity;detecting a decrease in a target entity.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, determining that a subject has or hasnot responded a particular treatment: detecting a target entity;identifying a target entity; detecting an increase in a target entity;detecting a decrease in a target entity.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, administering a particular treatment toa subject: detecting a target entity; identifying a target entity;detecting an increase in a target entity; detecting a decrease in atarget entity.

The treatment may optionally be treatment with any of the compoundslisted elsewhere herein, e.g., with one or more antimicrobial compounds.

Thus, optionally, the method may comprise determining or diagnosing onthe basis of the spectrometric data that a subject (i) has a microbialinfection; (ii) has an infection with a particular type of microbe;and/or (iii) has an infection with a microbe having sensitivity orresistance to one or more antimicrobials.

Determining which antimicrobial a microbe is sensitive to allows adetermination to be made as to which treatment a subject needs toreceive, and/or how urgently a subject needs to receive treatment.

Optionally, alternatively or in addition, the method may comprisedetermining or diagnosing, e.g., on the basis of the spectrometric data,that a or the subject (i) is in need of treatment with an antimicrobialagent; and/or (ii) is in need to treatment with an antimicrobial agentselected from an agent to which said microbe is sensitive.

Optionally, alternatively or in addition, the method may compriseadministering an antimicrobial agent to a or the subject, which mayoptionally be an antimicrobial agent to which the microbe causing theinfection is sensitive, as determined by the method provided herein.

Compounds

As discussed elsewhere herein is often desirable to analyse a compound.For example, the method may optionally be used to (i) detect thepresence of a compound; (ii) identify a compound; (iii) characterise acompound; and/or (iv) analyse the spatial distribution of a compound.This may optionally allow, e.g., the detection, identification and/orcharacterisation of a microbe that produces said compound; the analysisof the response to a microbial population to exposure to a compoundand/or environmental condition; the analysis of the production and/orbreakdown of a compound.

For example, the identity of compounds produced by microbes, e.g., in aresponse to a substance, environmental condition etc, is often unknown,so it may be desired to analyse such a compound. In the context offermentation, it may be desired to analyse the production of a primarycompound and/or any by-products.

Thus, the method may optionally involve the direct or indirect analysisof one or more substances. Unless otherwise stated, the terms“substance”, “compound”, “molecule” and “biomolecule” are usedinterchangeably herein.

As mentioned elsewhere herein, the method may also optionally involve astep of administering a treatment to a subject, e.g., a subjectsuffering from an infection by said microbial population. Such atreatment step may, e.g., involve the administration of a therapeuticagent, which may optionally comprise or consist of any of the substancesmentioned herein.

The compound may optionally be intracellular and/or extracellular. Itmay optionally be endogenous, i.e. produced by the microbial population,and/or exogenous, i.e. added to the microbial population.

The compound may, e.g., comprise or consist of a biomolecule, an organiccompound, and/or an inorganic compound. Optionally, it may be amicrobially-produced compound. It may optionally be an industrial wasteproduct, pollutant, herbicide, pesticide, explosive, therapeutic, fuel,food, and the like. For example, the substance may be abiopharmaceutical, e.g., antibody, hormone and/or cytokine.

The compound may optionally comprise or consist of any of the compoundsor classes of compounds mentioned herein, e.g., any of the biomarkercompounds mentioned herein. Thus, for example, it may optionally be aterpene; prenylquinone; sterol; terpenoid; alkaloid; glycoside;surfactin; 2-Heptyl-3-hydroxy-4(1H)-quinolone or2-heptyl-3,4-dihydroxyquinoline (“PQS” or Pseudomonas quinolone signal);4-hydroxy-2-heptylquinoline (“HHQ”); phenol, such as, a natural phenol;phenazine; biphenyl; dibenzofurans; beta-lactam; polyketide;rhamnolipid; cardiolipin; phosphatidylglycerol lipid; phosphatidic acids(PAs); phosphatidylethanolamines (PEs); phosphatidylglycerols (PGs);phosphatidylcholines (PCs); phosphatidylinositols (PIs);phosphatidylserines (PSs); sphingolipid; mycolic acids; ceramides,polyhydroxyalkanoates; diacylglycerol (DAG); and/or triacylglycerol(TAG).

Optionally, it may comprise or consist of, for example, a lipid, suchas, a glycolipid or phospholipid; carbohydrate; DNA; RNA; protein, e.g.,an antibody, enzyme or hormone; polypeptide, such as, a ribosomalpeptide or a non-ribosomal peptide; oligopeptide; lipoprotein;lipopeptide; amino acid; and/or chemical molecule, optionally an organicchemical molecule.

The compound may optionally be linear, cyclic or branched.

The compound may optionally be a metabolite, such as, a primary or asecondary metabolite; an antibiotic; a quorum sensing molecule; a fattyacid synthase product; a pheromone; a protein; a peptide; and/or abiopolymer. It may optionally be an antibody or hormone.

The compound may optionally be characterised by one or more of thefollowing functional groups: alcohol, ester, alkane, alkene, alkyne,ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic,carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it mayadditionally be identified as being primary, secondary or tertiary,e.g., a primary alcohol, a secondary amine, or the like.

The substance may optionally be a test agent or a drug.

The substance may optionally be a known drug, e.g., an anti-cancer drug,e.g., a cytostatic and/or cytotoxic agent, which may optionally beselected from any of the substances listed below.

The substance may optionally be, e.g., an aromatase inhibitor; ananti-angiogenic agent; a Tubulin-binding agent; an inhibitor oflipogenic pathways; and/or a cytostatic agent; optionally selected froman alkylating agent, a cross-linking agent, an intercalating agent, anucleotide analogue, an inhibitor of spindle formation, and/or aninhibitor of topoisomerase I and/or II.

It may, for example, be an antibody specific for a receptor expressed bycancer cells, which may optionally be conjugated to a chemotherapy drugor to a radioactive particle.

The antibody may optionally, for example, be selected from a HER-2/neuspecific monoclonal antibody, such as, Trastuzumab (Herceptin);Adecatumumab, alemtuzumab, Blinatumomab, Bevacizumab, Catumaxomab,Cixutumumab, Gemtuzumab, Rituximab, Trastuzumab, and/or Ibritumomab.

The substance may optionally be, e.g., an anthracycline, anEpipodophyllotoxin, a Dactinomycin, a Campthothecin, a Taxane, a Vincaalkaloid, Soraphen A, and/or Simvastatin

Cytotoxic anticancer drugs (sometimes known as antineoplastics) describea group of medicines that contain chemicals which are toxic to cells.The cytotoxic drugs prevent cell replication and growth and hence areuseful in the treatment of cancer. Most of the commonly used cytotoxicanticancer drugs were discovered through random high-throughputscreening of synthetic compounds and natural products in cell-basedcytotoxicity assays. Most of the compounds are DNA-damaging agents witha low therapeutic index.

The substance may optionally be selected from, e.g., anastrozole;azathioprine; bcg; bicalutamide; chloramphenicol; ciclosporin;cidofovir; coal tar containing products; colchicine; danazol;diethylstilbestrol; dinoprostone; dithranol containing products;dutasteride; estradiol; exemestane; finasteride; flutamide; ganciclovir;gonadotrophin, chorionic; goserelin; interferon containing products(including peginterferon); leflunomide; letrozole; leuprorelin acetate;medroxyprogesterone; megestrol; menotropins; mifepristone; mycophenolatemofetil; nafarelin; oestrogen containing products; oxytocin (includingsyntocinon and syntometrine); podophyllyn; progesterone containingproducts; raloxifene; ribavarin; sirolimus; streptozocin; tacrolimus;tamoxifen; testosterone; thalidomide; toremifene; trifluridine;triptorelin; valganciclovir; and/or zidovudine. These substances mayoptionally be referred to as non-chemotheraphy approvedcytotoxic/cytostatic drugs.

The substance may optionally be selected from, e.g., aldesleukin;alemtuzumab; amsacrine; arsenic trioxide; asparaginase; bleomycin;bortezomib; busulphan; capecitabine; carboplatin; carmustine; cetuximab;chlorambucil; cisplatin; cladribine; cyclophosphamide; cytarabine;dacarbazine; dactinomycin; daunorubicin; dasatinib; docetaxel;doxorubicin; epirubicin; estramustine; etoposide; fludarabine;fluorouracil; gemcitabine; gemtuzumab; hydroxycarbamide; idarubicin;ifosfamide; imatinib mesylate; irinotecan; lomustine; melphalan;mercaptopurine; methotrexate; mitomycin; mitotane; mitoxantrone;oxaliplatin; paclitaxel; pentamidine; pentostatin; procarbazine;raltitrexed; rituximab; temozolomide; thiotepa; topotecan; trastuzumab;vidaradine; vinblastine; and/or vincristine. These substances mayoptionally be referred to as non-chemotheraphy approvedcytotoxic/cytostatic drugs.

The substance may optionally be selected, e.g., from Mescaline, PCP(Phencyclidine), Psilocybin, LSD, Heroin, Morphine, Codeine,dextroamphetamine, bupropion, cathinone, lisdexamfetamine, Allobarbital,Alphenal (5-allyl-5-phenylbarbituric acid), Amobarbital, Aprobarbital,Brallobarbital, Butobarbital, Butalbital, Cyclobarbital,Methylphenobarbital, Mephobarbital, Methohexital, Pentobarbital,Phenobarbital, Secobarbital, Talbutal, Thiamylal, and/or Thiopental.Ranitidine, phenylalanine PKU, dimethylamylamine, cocaine, diazepam,androstadienedione, stigmastadienone, androsteronehemisuccinate,5α-androstan-3β,17β-diol-16-one, androsterone glucuronide,epitestosterone, 6-dehydrocholestenone, phenylalanine, leucine, valine,tyrosine, methionine, sitamaquine, terfenadine, prazosin, methadone,amitripyline, nortriptyline, pethidine, DOPA, ephedrine, ibuprofen,propranolol, atenolol, acetaminophen, bezethonium, citalopram,dextrorphan, paclitaxel, proguanil, simvastatin, sunitinib, telmisartan,verapamil, amitriptyline, pazopanib, tamoxifen, imatinib,cyclophosphamide, irinotecan, docetaxel, topotecan, acylcarnitines(C2-C18), nicotine, cotinine, trans-3′-hydroxycotinine, anabasine,amphetamine, amphetamine-like stimulants, methamphetamine, MDA, MDMA,MDEA, morphine, Δ⁹-THC, tacrolimus, benzethonium, meprobamate,O-desmethyl-cis-tramadol, carisoprodol, tramadol, nordiazepam, EDDP,norhydrocodone, hydromorphone, codeine, temazepam, noroxycodone,alprazolam, oxycodone, buprenorphine, norbuprenorphine, fentanyl,propoxyphene, 6-monoacetylmorphine, caffeine, carbadox, carbamazepine,digoxigenin, diltiazem, diphenhydramine, propanolol, sulfadiazine,sulfamethazine, sulfathiazole, thiabendazole, ketamine, norketamine,BZE, AMP, MAMP, and/or 6-MAM.

The methods of the invention may optionally involve the use of anantimicrobial, for example, to test for antibiotic resistance, or toprevent the growth of certain microbes, for example to preventcontamination.

The methods of the invention may optionally involve the detection and/orcharacterisation of antimicrobials, of microbes that produceantimicrobials, and/or of microbes that are sensitive or resistant toantimicrobials.

The substance may, e.g., be an antimicrobial. The term “antimicrobial”includes any agents that act against any type of microbe. Thus, theantimicrobial may optionally be selected from antibacterial, anantiviral, an antifungal, and an antiprotozoal. More particularly, itmay optionally be selected from aminoglycosides, beta-lactamantibiotics, chloramphenicol, fluroquinolones, glycopeptides,lincosamides, macrolides, polymixins, rifampins, streptogramins,sulphonamides, tetracyclines, and/or diaminopyrimidines.

The Aminoglycoside may optionally be selected from gentamicin,tobramycin, amikacin, streptomycin, kanamycin. The beta-lactamantibiotic may optionally be selected from a penicillin such asmethicillin, penicillin, amoxicillin, ampicillin, carbenicillin,oxacillin or nafcillin; a cephalosporin, such as, cephalothin,cefamandole, cefotaxime, ceftazidime, cefoperazone, or ceftriaxone; acarbapenem, such as, imipenem, meropenem, ertapenem, ordoripenem; or amonobactam, such as, aztreonam. The fluroquinolone may optionally beselected from Enrofloxacin, ciprofloxacin, Danofloxacin, Difloxacin,Ibafloxacin, Marbofloxacin, Pradofloxacin and Orbifloxacin. Theglycopeptide may optionally be selected from vancomycin, teicoplanin andavoparcin. The lincosamide may optionally be selected from Lincomycin,Clindamycin and Pirlimycin. The macrolide may optionally be selectedfrom Erythromycin, Tylosin, Spiramycin, Tilmicosin and Tulathromycin.The polymixin may optionally be selected from Polymixin B and colistin(Polymixin E). The rifampin may optionally be selected from Rifampin,Rifabutin and Rifapentine. The Streptogramin may optionally be selectedfrom Virginiamycin. The sulfonamide may optionally be selected fromSulfadiazine, sulfamethoxazole and sulfadoxine. The tetracycline mayoptionally be selected from Chlortetracycline, oxytetracycline,demethylchlortetracycline, rolitetracycline, limecycline, clomocycline,methacycline, doxycycline and minocycline. The Diaminopyrimidine mayoptionally be selected from Trimethoprim, Aditoprim, Baquiloprim and/orOrmetoprim.

The substance may, e.g., be an anti-viral drug.

The substance may, e.g., be an anti-inflammatory drug, optionallyselected from, e.g., steroids, diclofenac, ibuprofen, naproxen,celecoxib, mefenamic acid, etoricoxib, indomethacin, and/or aspirin.

Optionally, one or more microbial populations may be tested with a knownantimicrobial drug. Optionally, one or more microbial populations may betested using a panel of potentially new therapeutic agents orantimicrobial drugs.

Optionally, one or more microbial populations may be geneticallymodified and the modified microbial populations may be tested with aknown antimicrobial drug or against a panel of potentially newtherapeutic agents or antimicrobial drugs.

Automation and/or Optical Recognition

Optionally, the method may include automation. Optionally, the methodmay include the acquisition of an optical (or other) image of a sample,followed by (e.g., optical) recognition of one or more microbialpopulations, e.g., colonies, followed by sampling of one or moremicrobial populations (e.g., using the first device). Optionally, one ormore, e.g., all of these steps may be automated. Thus, the method mayoptionally include automatic sampling, which may optionally be carriedout using, e.g., a REIMS device. Any of the methods may optionallycomprise using a disposable sampling tip.

Imaging

The method provided herein may optionally comprise determining thespatial distribution of one or more microbes and/or compounds.

The method provided herein may optionally comprise determining thespatial distribution of one or more excreted substances emanating fromone or more microbes.

According to the various embodiments herein, ion imaging may be used togenerate an image or map of one or more properties of the target. Thismay be achieved by using the first device to generate aerosol, smoke orvapour from multiple different regions of the target; ionising analytesin the smoke, aerosol or vapour originating from the different regionsto produce analyte ions (or ions derived therefrom, e.g., fragmentions); and then analysing the analyte ions (or ions derived therefrom)to obtain spectrometric data for each of the regions of the target. Thespectrometric data is correlated to the region of the target to which itrelates (i.e. from where the smoke, aerosol or vapour that generated thespectrometric data originated from) so as to generate image or map data.An image or map of the target can then be generated based on the imageor map data. For example, one or more properties of each region of thetarget may be determined from the spectrometric data and this may beincluded in the image or map data and hence mapped as a function oflocation within the target. The image or map data may then be displayedto a user.

The first device may be stepped between multiple spaced apart regions ofthe target so as to generate the aerosol, smoke or vapour from discreteregions of the target. Alternatively, a plurality of devices may be usedto generate the aerosol, smoke or vapour from discrete regions of thetarget, optionally simultaneously. These plurality of devices may notmove across the target, although may move into and out of engagementwith the target. Alternatively, the first device may be moved across orthrough the target continuously so as to generate aerosol, smoke orvapour from the different regions of the target. Any movements of thefirst device, or the plurality of devices, may be automated andcontrolled by a machine.

The spectrometric data for each region may be analysed and convertedinto data representative of the type, condition or constituent(s) of thematerial at that region in the target.

The representative data may then be displayed as an image or map showingthe type, condition or constituents of the material as a function oflocation in the target.

For example, the representative data may indicate the type, level,presence and/or absence of a microbe and/or compound at each of theregions in the target. For example, the spectrometric data may be usedto identify and/or display the locations of margins of infected and/ornon-infected tissue in the target.

Additionally, or alternatively, the spectrometric data may be used toidentify and/or display the location of one or more microbe type ofinterest.

The representative data may indicate the different type of microbeand/or compound in the target.

Additionally, or alternatively, the representative data may indicate thepresence and/or distribution of one or more types of microbes within thetarget.

Additionally, or alternatively, the representative data may indicate thepresence and/or distribution of one or more types of compounds withinthe target.

Additionally, or alternatively, the representative data may indicate thetype or level of biomarker in the target, and the distribution of thetype or level of biomarkers within a target may be identified and/ordisplayed.

The ion imaging and map data may be generated and/or displayed inreal-time. This may be useful, for example, to determine action to betaken during surgical procedures. The position of at least a portion ofthe first device and/or another tool relative to the target may bedisplayed on the image or map, e.g., in real time. For example, theposition of a surgical tool, such as a tool for resecting or ablatingtissue, may be displayed on the map of the target. This enables thesurgeon to selectively resect or ablate tissue based on therepresentative data displayed in the image or map.

Ion imaging mass spectrometry technology, such as DESI-MS and/or REIMStechnology, may optionally be used to obtain the spectrometric data forthe different regions of the target. A REIMS technology device mayoptionally be used in cutting and/or pointing mode.

This ion imaging analysis may optionally be combined with a furtheranalysis of the target. Details of further analysis methods and toolsare provided elsewhere herein. Optionally, the results of massspectrometry imaging may be correlated with the results of a furtheranalysis.

More details as to how to perform ion imaging are discussed below withreference to a particular example of DESI imaging. It will be understoodthat the specific parameters discussed were those used in an assay bythe inventor, and that any of these parameters may be varied.

Specimens, such as tissue sections or microbes smeared onto the surfaceof a standard glass microscope slide, were subjected to DESI-MS imaginganalysis using an Exactive mass spectrometer (Thermo Fisher ScientificInc., Bremen, Germany). Exactive instrument parameters are listed in thetable below.

Thermo Exactive instrumental parameters used for DESI-MS imaging.Parameter Setting. Polarity negative Resolution 100,000 Mass range200-1050 Spray voltage −4.5 kV Capillary temperature 250° C. Capillaryvoltage −50 V Tube lens voltage −150 V Skimmer Voltage −24 V Max.injection time 1000 ms Microscans     1 AGC target 5e6Methanol/water (95:5 v/v) was used as the electrospray solvent at aflow-rate of 1.5 □L/min. Nitrogen N4.8 was used as nebulising gas at apressure of 7 bars. All solvents used were of LC-MS grade (Chromasolv,Sigma Aldrich, St Louis, Mo., USA). The height distance between the DESIsprayer and the sample surface was set to 2 mm with the distance betweenthe sprayer and sniffer set to 14 mm. The distance between the samplesurface and the inlet capillary of the mass spectrometer was <<1 mm. Theangle between the sprayer tip and the sample surface was set at 80°. Thecollection angle between inlet capillary and sample was set to 10°.

The general principle underlying imaging processes using DESI MS is thatrather than point-by-point sampling, horizontal line scans are performedover the specimen surface by moving the automated sampling platform at aspeed that covers the area determined as a pixel (spatial resolution) inthe time the mass spectrometer requires to complete one scan (acquireone mass spectrum). This results in each one file per row of theresulting image (number of rows determined by sample height divided byspatial resolution).

For image analysis, individual horizontal line scans were converted into.imzML files using the imzML Converter Version 1.1.4.5(www.maldi-msi.org). Single ion images and RGB images were generatedusing MSiReader Version 0.05(146) with linear interpolation (order 1)and 0.005 Da bin size.

Culture Media

The microbial population may be a microbial culture, so it may bemaintained in a culture medium. The culture medium may optionallycomprise a complex component, such as blood or a derivative thereof,e.g., serum, or be a serum-free defined medium. Unless it is desired totest an environmental factor relating to the culture medium, the culturemedium may optionally be sterile, isotonic, have a suitable pH, and/orcomprise all of the minerals and nutrients required by the microbialpopulation.

The microbe may optionally be cultured on a solid culture medium and itmay optionally be sampled directly from its solid culture medium. Itwill be understood that in the art the term “solid culture medium” isused to refer to non-liquids, which may, for example, be true solids orbe in gel form.

The microbe may optionally be cultured in liquid medium and it mayoptionally be processed to provide a solid sample as discussed elsewhereherein.

The culture medium may optionally comprise minerals; nutrients;indicators, such as, phenol red, and/or selective agents, such as aspecific antibiotic.

The culture medium may, for example, optionally comprise blood, serum,carbohydrate, and/or yeast extract.

The solid culture medium may optionally comprise a solidifying agent ora matrix selected from agar, which is a phycocolloid that may beextracted from a group of red-purple marine algae; cassava starchpowder; methylcellulose; a collagen matrix; or any other suitablepolymer. Agar may, for example, optionally be used in a finalconcentration of 1-2% for solidifying culture media.

The culture medium may, for example, optionally be selected from aliquid or solid (such as agar) form of any of the following commonlyknown media: Luria Bertani (LB), Brain-heart infusion, Columbia horseblood, Chocolate, Mueller-Hinton, Trypticase soy, Aztreonam, Braziersmedia, Fastidious anaerobe, Eosin methylene blue, Mannitol salt, and/orMacConkey.

One or more of the following culture medium components may optionally beoptimised or altered: (i) Nutrients, which may be optimised to includeall essential nutrients at sufficient levels, or which may be altered,e.g., to insufficient levels of one or more nutrients; minerals whichmay be optimised to include all essential minerals at sufficient levels,or which may be altered, e.g., to insufficient levels of one or moreminerals; (ii) pH; (iii) temperature; and/or (iv) levels of gases, e.g.,oxygen and/or CO₂, to which the microbial population is exposed. Optimumculture conditions will depend on the type of microbe. For example, apathogenic microbe may have an optimum growth temperature of about30-37° C., whereas a thermophilic microbe may have higher optimum growthtemperature.

Nutrients

Microbial populations require nutrients for survival and/or growth. Oneor more suitable nutrients may therefore be used to culture a microbialpopulation. Optionally, a mixture of different nutrients may be used,e.g., a mixture comprising one or more of the nutrients listed below. Asdiscussed elsewhere herein, the type and/or level of any nutrients maybe altered, e.g., when analysing the effect of environmental conditions.

Any nutrient may optionally be a heavy-isotope nutrient.

Suitable nutrients are well known, but a nutrient may optionallycomprise or consist of, e.g., a carbohydrate, optionally selected frommonosaccharides, disaccharides, oligosaccharides, and/orpolysaccharides. It may optionally be selected from sucrose, glucose,fructose, maltose, starch, lactose, galactose, lactulose, and/ortrehalose.

A nutrient may optionally comprise or consist of, e.g., an amino acid, apeptide, a polypeptide, or protein, optionally selected from anessential amino acid, a non-essential amino acid, and/or a peptide,polypeptide or protein comprising one or more essential and/ornon-essential amino acids. Essential amino acids may be selected fromphenylalanine, valine, threonine, tryptophan, methionine, leucine,isoleucine, lysine, and/or histidine. Optionally, a nutrient may beglutamine.

A nutrient may optionally comprise or consist of a vitamin, e.g.,vitamin A, B, C, D, or E.

A nutrient may optionally comprise or consist of a lipid, e.g., a fattyacid, lecithin, and/or a sterol.

Flow Cytometry

Optionally, the method may additionally include a step of flowcytometry, e.g., prior to and/or after the mass and/or ion mobilityspectrometric analysis. For example, the method may optionally becarried out on a microbial population that was previously analysed viaflow cytometry, e.g., it may optionally be carried out on asub-population of microbes sorted via fluorescence-assisted cell sorting(“FACS”).

Optionally, the method may comprise separating labelled microbes fromunlabelled microbes prior into a first and a second subset, optionally alabelled and an unlabelled subset. This separation may optionally bebefore and/or after the generation of spectrometric data. Thus,optionally, the target may be a subset of a microbial population,wherein the subset has been generated using flow cytometry, e.g., FACS.

Optionally, said steps of separating labelled cells from unlabelledmicrobes may be carried out prior to performing the method providedherein. Optionally, (i) at least one of said subsets may be analyseddirectly via the method of any preceding claim; (ii) at least one subsetmay be introduced directly into a mass spectrometer and/or ion mobilityspectrometer; and/or (iii) a FACS device may be coupled, optionallydirectly, to a device, optionally as defined elsewhere herein, e.g., amass spectrometer and/or ion mobility spectrometer.

In biotechnology, flow cytometry is a laser-based biophysical technologyemployed, e.g., in cell counting, cell sorting, biomarker detection andprotein engineering. Flow cytometry may optionally be used, e.g., foranalysing the expression of microbial cell surface and/or intracellularmolecules, characterizing and/or identifying different microbe types ina heterogeneous microbe population, assessing the purity of isolatedsubpopulations, and/or analysing cell size and volume. It allowssimultaneous multi-parameter analysis of single microbial cells.

It may particularly be used to measure fluorescence intensity producedby ligands that bind to specific cell-associated molecules, e.g., (i)fluorescent-labelled antibodies detecting proteins; or (ii) propidiumiodide binding to DNA.

The staining procedure may involve making a single-cell suspension froma microbe culture or sample. The microbes may then incubated, e.g., intubes and/or microtiter plates, with unlabelled or fluorochrome-labelledantibodies. Microbes may be suspended in a stream of fluid and passed byan electronic detection apparatus.

Flow cytometers are able to analyse several thousands or particles persecond. A flow cytometer comprises a flow cell in which a liquid streamcarries and aligns cells so that they pass single file through a lightbeam for sensing. The impedance or conductivity of the cells and variousoptical properties of the cells may be measured.

Flow cytometry is routinely used in the diagnosis of health disorders,especially blood cancers, but has many other applications in basicresearch, clinical practice and clinical trials. A common variation isto physically sort particles based on their properties, so as to purifypopulations of interest, e.g., by fluorescence-assisted cell sorting(“FACS”).

Fluorescence-activated cell sorting (FACS) is a method of sorting aheterogeneous mixture of biological cells, e.g., microbial cells, intotwo or more containers, one cell at a time, based upon the specificlight scattering and fluorescent characteristics of each cell. Thus,FACS allows the physical sorting of a heterogeneous mixture of cellsinto 2 or more different sub-populations.

As mentioned above, microbial cells may be labelled with fluorescentlabels that are specific for a particular cellular marker. If a cellpopulation is heterogeneous for that marker only the marker-positivesubpopulation of the cells will become labelled.

A FACS apparatus may then be used to sort the cells. The cell suspensionis entrained in the centre of a narrow, rapidly flowing stream ofliquid. The flow is arranged so that there is a large separation betweencells relative to their diameter. A vibrating mechanism causes thestream of cells to break into individual droplets. The system isadjusted so that there is a low probability of more than one cell perdroplet. Just before the stream breaks into droplets, the flow passesthrough a fluorescence measuring station where the fluorescent characterof interest of each cell is measured. An electrical charging ring isplaced just at the point where the stream breaks into droplets. A chargeis placed on the ring based on the immediately prior fluorescenceintensity measurement, and the opposite charge is trapped on the dropletas it breaks from the stream. The charged droplets then fall through anelectrostatic deflection system that diverts droplets into containersbased upon their charge. In some systems, the charge is applied directlyto the stream, and the droplet breaking off retains charge of the samesign as the stream. The stream is then returned to neutral after thedroplet breaks off.

Fluorescent Labelling

Optionally, a molecule on or within one or more of the microbial cellsmay be labelled. The label may, e.g., be a fluorescent label.

The following table details a list of fluorochromes forimmunofluorescence microscopy:

Fluorochromes excitation (nm) emission (nm) AMCA 347 445 Alexa Fluor 350345 440 Alexa Fluor 488 488 520 Cy2 492 510 FITC 496 518 Bodipy-FL 503511 TRITC 544 572 Cy3 550 570 LRSC 572 590 Rhodamine Red-X 570 590 TexasRed 596 620 Cy5 650 670 Alexa Fluor 647 650 668wherein AMCA is aminomethylcoumarin acetic acid, Cy2 is cyanine, FITC isfluorescein isothiocyanate, TRITC is tetramethylrhodamineisothiocyanate, Cy3 is indocarbocyanine, LRSC is lissamine rhodaminesulfonyl chloride and Cy5 is indodicarbocyanine.

Further Analytical Tools

Any of the methods of the invention may optionally include a step ofusing one or more additional analytical tools to detect, identify and/orcharacterise a microbe. Such a tool may, for example, be selected frommicroscopic examination; Gram-staining; examination of the morphology ofa microbial colony or an individual microbe; nucleic acid analysis, forexample, using restriction enzymes, hybridisation, polymerase chainreaction (PCR) amplification and/or sequencing; morphologicalexamination; culture-based screening for nutrient requirements and/orantimicrobial sensitivity; fatty acid profiling; and/or testing forantigens. Such tools are well known in the art, but brief details areprovided below.

Microscopic examination may, for example, optionally involve mounting amicrobial sample on a microscopic slide, for example, by placing a dropof microbe-containing solution onto the slide or by smearing a samplefrom a microbial colony or other microbial-containing material onto aslide together with a drop of water.

Gram-staining may, for example, optionally involve heat-fixing amicrobial sample on a microscopic slide, for example, by gently movingthe slide over a heat source such as a Bunsen-burner. A dye, typicallyCrystal Violet, may then be applied. This dye typically penetratesthrough the cell wall and cell membrane of both Gram-positive andGram-negative cells and stains the bacterial cells purple. Iodine maythen be added. Iodine acts as a mordant and as a trapping agent. Amordant is a substance that increases the affinity of the cell wall fora stain by binding to the primary stain, thus forming an insolublecomplex which gets trapped in the cell wall. In the Gram stain reaction,the crystal violet and iodine form an insoluble complex which serves toturn the smear a dark purple colour. At this stage, all cells will turnpurple. A suitable alcohol, such as 95% ethyl alcohol, or acetone, maythen be added to dissolve the lipid outer membrane of Gram negativebacteria, thus leaving the peptidoglycan layer exposed and increasingthe porosity of the cell wall. The Crystal Violet-Iodine complex is thenwashed away from the thin peptidoglycan layer of Gram negative bacteria,leaving them colourless. By contrast, in Gram positive bacteria theCrystal Violet-Iodine complex gets tightly bound into the multi-layered,highly cross-linked Gram positive cell wall, thus staining the cellspurple. Optionally, any decolorized Gram negative cells can then berendered visible with a suitable counterstain, such as positivelycharged safranin, which stains them pink.

Nucleic acid analysis may optionally involve isolation and purificationof DNA and/or RNA.

Nucleic acid analysis via PCR amplification may, for example, optionallyinvolve amplification of all or part of a suitable gene, such as thebacterial 16S rRNA gene, using universal or species-specific primers.Other examples of suitable genes which may optionally be analysedalternatively or in addition include, for example, species-specificgenes or virulence genes, for example, Shiga toxin (stx), intimin (eae),flagellar H-antigen genes fliC-fliA, hsp65, rpoB and/or recA. For fungi,PCR amplification of all or part of the internal transcribed spacer(ITS) is particularly suitable.

Nucleic acid analysis with restriction enzymes may, for example,optionally involve restriction-fragment length polymorphism (RFLP)analysis. RFLP, is a technique that exploits variations in the length ofhomologous DNA sequences. RFLP analysis may involve a restrictiondigest, i.e. incubating a DNA with a suitable restriction enzyme such asBamHI, HindIII or EcoRI. Each restriction enzyme can recognise and cut aspecific short nucleic acid sequence. The resulting DNA fragments maythen be separated by length, for example, through agarose gelelectrophoresis. The DNA fragments in the gel may optionally be stained,for example, with ethidium bromide, and the pattern of the fragments ofdifferent length may be determined.

Optionally, the DNA fragment may be transferred to a membrane via theSouthern blot procedure. The membrane may then be exposed to a labelledDNA probe to allow hybridisation to occur. The label may, for example,be or comprise a radioactive isotope or digoxigenin (DIG). Anyunhybridised probe may then be washed off. The label may then bedetected and the pattern of the fragments which have hybridised to thelabelled probe may be determined.

Sequencing may, for example, optionally involve the dideoxy or chaintermination method. In this method, the DNA may be used as a template togenerate a set of fragments that differ in length from each other by asingle base. The fragments may then be separated by size, and the basesat the end may be identified, recreating the original sequence of theDNA.

Hybridisation analysis may, for example, optionally include DNA-DNAhybridization of one or more selected DNA fragments, genes or wholegenomic DNA from a first microbe to a labelled DNA probe to determinethe genetic similarity between the first microbe and the known orcomparator microbe. Hybridisation analysis may, for example, involvetransfer of the DNA to a membrane via the Southern blot procedure,labelling and detection as described above.

Fatty acid profiling of microbes may, for example, optionally be carriedout using gas-chromatography coupled to a flame ionisation detector(GC-FID), or high performance liquid chromatography (HPLC).

With respect to the colony morphology, one or more of the following may,for example, optionally be examined: size; whole colony shape, whichmay, for example, be circular, irregular, or rhizoid; colony edge, whichmay, for example, be smooth, filamentous, or undulating; elevation,which may, for example, be flat, raised, convex or crateriform; surface,which may, for example, be wrinkled, rough, waxy, or glistening;opacity, which may, for example, be transparent, translucent, or opaque;pigmentation; colour, which may, for example, be red, yellow, or white;and/or water solubility.

With respect to the morphology of individual microbes, this may, forexample, optionally be determined to be a coccus (spherical), bacillus(rod-shaped), spiral (twisted), or pleomorphic. Cocci may optionally bea single coccus, diplococcic, streptococci, tetrads, sarcinae orstaphylococci. Bacilli may optionally be a single bacillus,diplobacilli, streptobacilli or coccobacilli. Spirals may optionally bevibrio, spirilla or Spirochetes.

Culture-based screening for nutrient requirements may optionally involveinoculating microbes onto on into one or more different growth media,such as different selective media, and observing in/on which mediamicrobial growth occurs, and to what extent the growth differs betweendifferent media.

Culture-based screening for antimicrobial sensitivity may optionallyinvolve inoculating microbes onto one or more different growth media,which may be done, for example, by streaking or plating the microbesonto a petri dish containing a suitable nutrient agar. An antimicrobialagent may then be added, which may be done, for example, by placing afilter paper disk impregnated with the antimicrobial onto the growthmedium. Several disks each containing a different antimicrobial agentmay be added onto a single petri dish. A determination may then be madeas to whether a zone of growth inhibition occurs around any of thedisk(s), and, if so, how large this zone is.

Testing for antigens may also be referred to as serotyping. The presenceof specific antigens, particularly on the cell surface of the microbe,may be tested for by using specific antibodies. The antibodies may bepolyclonal or monoclonal. The test may optionally involve simplydetecting the presence or absence of agglutination, i.e. the formationof complexes of microbes and antibodies. Alternatively or in addition,the antibodies may be

Labelled and the assay may involve, for example, an enzyme-linkedimmunosorbent assay (“ELISA”) and/or fluorescence activated cell sorting(“FACS”).

Further aspects and embodiments are set out below.

According to an aspect there is provided a method of ion imaging. Themethod includes automatically sampling using a rapid evaporationionization mass spectrometry (“REIMS”) device a plurality of differentlocations of a bacterial and/or a fungal sample which has been culturedon to a culture medium; obtaining spectrometric data corresponding toeach location; and using the obtained spectrometric data to identify oneor more bacterial strains and/or one or more fungal strains at each thelocation.

Optionally, the method may additionally include a step of flowcytometry, e.g., prior to and/or after the mass and/or ion mobilityspectrometric analysis. For example, the method may optionally becarried out on a cell population that was previously analysed via flowcytometry, e.g., it may optionally be carried out on a sub-population ofcells sorted via fluorescence-assisted cell sorting (“FACS”).

Strittmatter discloses a manual approach wherein as described on p. 6556two hand-held electrodes in the form of a forceps were used as asampling probe. Strittmatter does not disclose a method of automaticallysampling.

WO 2010/136887 (Takats) does not disclose automatically samplingdifferent locations on a cultured medium.

The approach according to an embodiment was validated using samples ofhuman liver with metastases and bacterial strains, cultured on solidmedium, belonging to the species P. aeruginosa, B. subtilis and S.aureus. For both sample types, spatially resolved spectral informationwere obtained that resulted in clearly distinguishable multivariateclustering between the healthy/cancerous liver tissues and between thebacterial species.

The culture medium may comprise an agar-based medium, a carbohydratematrix or another solid growth medium, as discussed elsewhere herein.

The method may further comprise determining the spatial distribution ofone or more excreted substances emanating from one or more bacterialcolonies and/or fungal colonies which have been cultured on the medium.

The one or more excreted substances may be selected from any of thecompounds discussed elsewhere herein, optionally more particularly thegroup consisting of: (i) one or more metabolites; (ii) one or moreprimary metabolites; (iii) one or more secondary metabolites; (iv) oneor more lipopeptides; (v) surfactin; (vi) one or more quorum sensingmolecules; (vii) 2-Heptyl-3-hydroxy-4(1H)-quinolone or2-heptyl-3,4-dihydroxyquinoline (“PQS” or Pseudomonas quinolone signal);(viii) 4-hydroxy-2-heptylquinoline (“HHQ”); (ix) one or moreantibiotics; (x) one or more alkaloids; (xi) one or more terpenoids;(xii) one or more glycosides; (xiii) one or more natural phenols; (xiv)one or more phenazines; (xv) one or more biphenyls and dibenzofurans;(xvi) one or more beta-lactams; (xvii) one or more polyketides; (xviii)one or more fatty acid synthase products; (xix) one or more nonribosomalpeptides; and (xx) one or more ribosomal peptides.

The step of automatically sampling a plurality of different locations ofa bacterial and/or fungal sample may comprise sampling using adisposable tip.

According to another aspect there is provided an ion imager. The ionimager includes a rapid evaporation ionization mass spectrometry(“REIMS”) device which is arranged to automatically sample a pluralityof different locations of a bacterial and/or a fungal sample which hasbeen cultured on to a culture medium; and a mass analyser arranged andadapted: (i) to obtain spectrometric data corresponding to each thelocation; and (ii) to use the obtained spectrometric data to identifyone or more bacterial strains and/or one or more fungal strains at eachthe location.

The culture medium may comprise an agar-based medium, a carbohydratematrix or another solid growth medium.

The ion imager may be arranged and adapted to determine the spatialdistribution of one or more excreted substances emanating from one ormore bacterial colonies and/or fungal colonies which have been culturedon the medium.

The one or more excreted substances may be selected from the groupconsisting of: (i) one or more metabolites; (ii) one or more primarymetabolites; (iii) one or more secondary metabolites; (iv) one or morelipopeptides; (v) surfactin; (vi) one or more quorum sensing molecules;(vii) 2-Heptyl-3-hydroxy-4(1H)-quinolone or2-heptyl-3,4-dihydroxyquinoline (“PQS” or Pseudomonas quinolone signal);(viii) 4-hydroxy-2-heptylquinoline (“HHQ”); (ix) one or moreantibiotics; (x) one or more alkaloids; (xi) one or more terpenoids;(xii) one or more glycosides; (xiii) one or more natural phenols; (xiv)one or more phenazines; (xv) one or more biphenyls and dibenzofurans;(xvi) one or more beta-lactams; (xvii) one or more polyketides; (xviii)one or more fatty acid synthase products; (xix) one or more nonribosomalpeptides; and (xx) one or more ribosomal peptides.

The ion imager may be arranged and adapted to use a disposable tip toautomatically sample a plurality of different locations of a bacterialand/or a fungal sample.

According to another aspect there is provided a method of RapidEvaporation Ionization Mass Spectrometry (“REIMS”). The method includesusing a REIMS ionisation source to analyse a biological liquid for thepresence or absence of bacteria in the biological liquid.

The biological liquid may be selected from the group consisting of: (i)blood; (ii) urine; (iii) saliva; (iv) sputum; or (v) serum.

The method may further comprise using a disposable sampling tip tosample the biological liquid.

The method may further comprise aspirating or passing the biologicalliquid through a filter media.

The method may further comprise analysing residue on the filter mediawhich remains after the biological liquid has been aspirated or passedthrough the filter media.

According to another aspect there is provided an apparatus. Theapparatus comprises a Rapid Evaporation Ionization Mass Spectrometry(“REIMS”) device which is arranged and adapted to analyse a biologicalliquid for the presence or absence of bacteria in the biological liquid.

The biological liquid may be selected from the group consisting of: (i)blood; (ii) urine; (iii) saliva; (iv) sputum; or (v) serum.

The apparatus may further comprise a disposable sampling tip to samplethe biological liquid.

The apparatus may further comprise a device which is arranged andadapted to aspirate or pass the biological liquid through a filtermedia.

The apparatus may further comprise an analyser which is arranged andadapted to analyse residue on the filter media which remains after thebiological liquid has been aspirated or passed through the filter media.

According to another aspect there is provided a method. The methodincludes obtaining an optical image of a substrate and determining onthe basis of the optical image if one or more areas of interest exist onthe substrate; wherein if one or more areas of interest are determinedto exist, then the method further comprises the steps of: (i)automatically sampling at least one location within at least onedetermined area of interest using a rapid evaporation ionization massspectrometry (“REIMS”) device and obtaining spectrometric datacorresponding to the at least one location; and (ii) using the obtainedspectrometric data to identify one or more bacterial strains and/or oneor more fungal strains at the one or more locations.

The substrate may comprise a food product.

According to another aspect there is provided an apparatus. Theapparatus includes a rapid evaporation ionization mass spectrometry(“REIMS”) device; a device arranged and adapted to obtain an opticalimage of a substrate; and a control system arranged and adapted: (i) todetermine on the basis of the optical image if one or more areas ofinterest exist on the substrate, wherein if one or more areas ofinterest are determined to exist, then the control system is furtherarranged and adapted to: (ii) to automatically sample at least onelocation within at least one determined area of interest using the rapidevaporation ionization mass spectrometry (“REIMS”) device and to obtainspectrometric data corresponding to the at least one location; and (iii)to use the obtained spectrometric data to identify one or more bacterialstrains and/or one or more fungal strains at the one or more locations.

The substrate may comprise a food product.

According to another aspect there is provided a method of ion imaging.The method includes dispensing a bacterial and/or fungal sample onto aculture medium, wherein one or more antibiotic and/or antifungalsubstances are embedded within and/or on the culture medium;automatically sampling using a rapid evaporation ionization massspectrometry (“REIMS”) device a plurality of different locations of thebacterial and/or a fungal sample which has been cultured on the culturemedium; obtaining spectrometric data corresponding to each the location;and determining from the spectrometric data information concerning theresistance or otherwise of the sample to the one or more antibioticand/or antifungal substances.

According to another aspect there is provided an ion imager. The ionimager includes a rapid evaporation ionization mass spectrometry(“REIMS”) device; and a control system arranged and adapted: (i) toautomatically sample using the rapid evaporation ionization massspectrometry (“REIMS”) device a plurality of different locations of abacterial and/or a fungal sample which has been cultured on a culturemedium, wherein one or more antibiotic and/or antifungal substances areembedded within and/or on the culture medium; (ii) to obtainspectrometric data corresponding to each the location; and

(iii) to determine from the spectrometric data information concerningthe resistance or otherwise of the sample to the one or more antibioticand/or antifungal substances.

According to another aspect there is provided a method of ion imaging.The method includes automatically sampling a plurality of differentlocations on a sample using a rapid evaporation ionization massspectrometry (“REIMS”) device and obtaining spectrometric datacorresponding to each location; and using the obtained spectrometricdata to construct, train or improve a sample classification model.

According to an embodiment the sample may comprise a biological sample,biological tissue, human tissue, animal tissue, one or more bacterialstrains or one or more fungal stains.

A sample classification model may be used comprising a biological sampleclassification model, a biological tissue classification model, a humantissue classification model, an animal tissue classification model, abacterial strain classification model or a fungal strain classificationmodel.

The method may further comprise automatically translating a samplerelative to the REIMS device optionally before, optionally during andoptionally after obtaining spectrometric data from at least some of thelocations on the sample.

The REIMS device may comprise one or more electrodes or one or moreelectrosurgical tips.

The one or more electrodes or the one or more electrosurgical tips maycomprise a monopolar device.

According to an embodiment a separate return electrode may be provided.

The one or more electrodes or the one or more electrosurgical tips maycomprise a bipolar device.

The step of automatically sampling a plurality of different locations onthe sample may further comprise applying an RF voltage to the one ormore electrodes or the one or more electrosurgical tips.

The RF voltage may have an amplitude, a peak to peak voltage or a RMSvoltage selected from the group consisting of: (i) about <100 V; (ii)about 100-200 V; (iii) about 200-300 V; (iv) about 300-400 V; (v) about400-500 V; (vi) about 500-600 V; (vii) about 600-700 V; (viii) about700-800 V; (ix) about 800-900 V; (x) about 900-1000 V; and (xi) about >1kV.

The RF voltage may have a frequency selected from the group consistingof: (i) about <1 kHz; (ii) about 1-2 kHz; (iii) about 2-3 kHz; (iv)about 3-4 kHz; (v) about 4-5 kHz; (vi) about 5-6 kHz; (vii) about 6-7kHz; (viii) about 7-8 kHz; (ix) about 8-9 kHz; (x) about 9-10 kHz; (xi)about 10-20 kHz; (xii) about 20-30 kHz; (xiii) about 30-40 kHz; (xiv)about 40-50 kHz; (xv) about 50-60 kHz; (xvi) about 60-70 kHz; (xvii)about 70-80 kHz; (xviii) about 80-90 kHz; (xix) about 90-100 kHz; (xx)about 100-200 kHz; (xxi) about 200-300 kHz; (xxii) about 300-400 kHz;(xxiii) about 400-500 kHz; (xxiv) about 500-600 kHz; (xxv) about 600-700kHz; (xxvi) about 700-800 kHz; (xxvii) about 800-900 kHz; (xxviii) about900-1000 kHz; (xxix) about 1-2 MHz; and (xxx) about >2 MHz.

The method may further comprise aspirating analyte, smoke, fumes,liquid, gas, surgical smoke, aerosol or vapour produced from a sample.

The method may comprise aspirating the analyte, smoke, fumes, liquid,gas, surgical smoke, aerosol or vapour in a substantially pulsed manner.

The method may comprise aspirating the analyte, smoke, fumes, liquid,gas, surgical smoke, aerosol or vapour substantially only when anelectrosurgical voltage or potential is supplied to one or moreelectrodes or one or more electrosurgical tips.

The method may comprise varying an aspiration duty cycle during thecourse of a surgical, non-surgical or other procedure.

The method may comprise passing the analyte, smoke, fumes, liquid, gas,surgical smoke, aerosol or vapour into a vacuum chamber of a massspectrometer.

The method may comprise causing at least some of the analyte, smoke,fumes, liquid, gas, surgical smoke, aerosol or vapour to impact upon acollision surface located within a vacuum chamber of the massspectrometer.

At least some of the analyte, smoke, fumes, liquid, gas, surgical smoke,aerosol or vapour may be ionised upon impacting the collision surface soas to form analyte ions.

The method may comprise heating the collision surface.

The step of heating the collision surface may comprise heating thecollision surface to a temperature selected from the group consistingof: (i) about <100° C.; (ii) about 100-200° C.; (iii) about 200-300° C.;(iv) about 300-400° C.; (v) about 400-500° C.; (vi) about 500-600° C.;(vii) about 600-700° C.; (viii) about 700-800° C.; (ix) about 800-900°C.; (x) about 900-1000° C.; (xi) about 1000-1100° C.; and (xii)about >1100° C.

The method may comprise mass analysing the analyte ions.

The method may comprise adding a matrix to the analyte, smoke, fumes,liquid, gas, surgical smoke, aerosol or vapour.

The matrix may be added to the analyte, smoke, fumes, liquid, gas,surgical smoke, aerosol or vapour prior to the analyte, smoke, fumes,liquid, gas, surgical smoke, aerosol or vapour impacting upon thecollision surface.

The matrix may be selected from the group consisting of: (i) a solventfor the analyte, smoke, fumes, liquid, gas, surgical smoke, aerosol orvapour; (ii) an organic solvent; (iii) a volatile compound; (iv) polarmolecules; (v) water; (vi) one or more alcohols; (vii) methanol; (viii)ethanol; (ix) isopropanol; (x) acetone; and (xi) acetonitrile.

The matrix may comprise a lockmass or calibration compound.

The method may comprise operating the REIMS device in a cutting mode ofoperation wherein the REIMS device forms one or more substantiallycontinuous cuts in the sample.

The method may comprise maintaining the REIMS device at substantiallythe same height whilst performing the one or more substantiallycontinuous cuts in the sample.

The method may comprise maintaining the REIMS device in substantiallycontinuous contact with the sample whilst performing the one or moresubstantially continuous cuts in the sample.

The method may comprise operating the REIMS device in a pointing mode ofoperation.

The method may comprise lowering the REIMS device so as to contact thesample and to acquire spectrometric data and then raising the REIMSdevice after contacting the sample and prior to acquiring furtherspectrometric data.

The method may comprise obtaining an optical image of the sample.

The method may comprise substantially co-registering the optical imageand an ion image.

The method may comprise defining one or more regions of interest in theoptical image and/or the ion image.

The method may comprise determining a class or classification of one ormore regions of interest.

The class or classification may comprise a healthy status, apre-cancerous status, a cancerous status, a bacterial strain or a fungalstrain.

According to another aspect there is provided a method. The methodincludes sampling a plurality of different locations of a sample using arapid evaporation ionization mass spectrometry (“REIMS”) device and toobtain spectrometric data at each the location; and using a sampleclassification model which was previously constructed, trained orimproved in order to classify the sample at each the location.

According to another aspect there is provided an ion imager. The ionimager includes a rapid evaporation ionization mass spectrometry(“REIMS”) device; and a control system arranged and adapted: (i) toautomatically sample a plurality of different locations on a sampleusing the rapid evaporation ionization mass spectrometry (“REIMS”)device and to obtain spectrometric data corresponding to each thelocation; and (ii) to use the obtained spectrometric data to construct,train or improve a sample classification model.

According to another aspect there is provided an apparatus. Theapparatus includes a rapid evaporation ionization mass spectrometry(“REIMS”) device; and a control system arranged and adapted: (i) tosample a plurality of different locations of a sample using the rapidevaporation ionization mass spectrometry (“REIMS”) device and to obtainspectrometric data at each the location; and (ii) to use a sampleclassification model which was previously constructed, trained orimproved in order to classify the sample at each the location.

Automatic Ion Imaging of Bacterial Samples

For the analysis of human samples, ethical approval was obtained fromthe National Healthcare Service Research Ethics Committee (Study ID11/LO/1686).

According to various embodiments an automatic ion imager was providedwhich was arranged to automatically sample different locations of atarget (e.g., a bacterial or fungal sample which had been culture on aculture medium).

FIG. 1 shows a related embodiment wherein a REIMS imaging platform islocated above a tissue sample to be imaged.

FIG. 2 shows a workflow illustrating various aspects an embodimentwherein fresh human liver metastasis samples were obtained from surgicalresection specimens and immediately frozen to −80° C. The tissue sampleswere cryosectioned (Thermo Microm HM550 Cryostat, Thermo FisherScientific®, Germany) to 10 μm thickness and thaw mounted onto glassslides for Desorption Electrospray Ionisation (“DESI”) analysis. Theremaining bulk tissue was used for REIMS analysis.

DESI analysis was carried out using an in-house built DESI stage andREIMS analysis was performed using a modified Prosolia® flowprobe stage(Prosolia®, USA).

DESI analysis of tissues was carried out using a mass spectrometeroperated in negative ion mode.

The DESI imaging pixel size was set to 100 μm, the electrospray solventwas methanol:water (95:5 vol/vol) at a solvent flow rate of 1.5 μL/minand zero-grade nitrogen nebulizing gas at a pressure of 4 bar was used.Following DESI analysis, tissue sections were stained with H&E(haematoxylin and eosin) and digitally scanned (Nano-Zoomer 2.0-HT,Hamamatsu®, Japan) to create optical images for comparison with MSimages.

A line scan mode (cutting mode) REIMS analysis of one liver metastasissample was performed on a mass spectrometer and a spot sampling(pointing mode) analysis of another liver metastasis sample and amicroorganism culture were performed on a Waters Xevo G2-S Q-TOFinstrument® (Waters Micromass®, U.K.) in negative ion mode.

The Waters Xevo G2-S® mass spectrometer was equipped with a modifiedatmospheric interface combining an orthogonal Venturi-pump for aerosoltransfer and a heated capillary inlet as shown in FIG. 3 .

REIMS imaging analysis of liver metastasis was carried out in a (first)cutting mode at about 1 bar Venturi gas pressure and about 4 kV p-pamplitude at about 50 kHz alternating current frequency (AC). Ablade-shaped electrosurgical tip was used, about 500 μm pixel size,about 1 mm/s cutting speed and about 1 mm cutting depth.

Analysis of liver metastasis in a (second) pointing mode was carried outat about 0.25 bar Venturi gas pressure, about 2 kV amplitude at about 50kHz AC and using a wire-shaped electrosurgical tip at about 750 μm pixelsize, about 0.1 s time remaining inside the sample and a pointing depthof about 1 mm.

Aerosol was transferred using a ⅛″ OD, 2 mm ID PTFE tubing. Since theused power settings were sufficiently high such as potentially to causesevere injury, the instrumental setup was handled with high caution andinsulating gloves were worn.

Parameter optimization of the REIMS imaging platform was carried outusing porcine liver samples. For comparison of spectrometric patternsbetween REIMS imaging and iKnife, porcine liver, porcine kidney cortex,lamb liver and chicken skeletal muscle were analysed using anelectrosurgical handpiece (Meyer-Haake GmbH®, Germany) with incorporatedPTFE tubing (⅛″ OD, 2 mm ID) which was connected to the Venturi pump.Liver, kidney and muscle were food grade and purchased as such. TheiKnife was operated in a cutting mode at 4 about 0 W and about 1 bar gaspressure in combination with a Valleylab SurgiStat II® power-controlledelectrosurgical generator (Covidien, Ireland).

Data Processing

Raw spectral profiles were loaded into a MATLAB® environment (VersionR2014a, Mathworks, USA) for pre-processing, MS-image visualization andpattern recognition analysis. All mass spectra were linearlyinterpolated to a common interval of 0.1 Da and individually normalizedto the total ion count (“TIC”) of each mass spectrum. The data was usedfor univariate comparison of intensity levels across liver tissue typesand ionization techniques and for bacterial MS-image visualization ofsingle ions. Peak annotation for liver metastasis samples was based onm/z accuracy obtained from the unprocessed raw files, while bacterialpeak annotation was based on mass accuracy and on tandem-MS spectraobtained using bipolar forceps.

Multivariate MS-image visualization was performed on mass spectraadditionally binned to 1 Da intervals in the mass range of m/z 600-1000Da for biological tissue and m/z 400-2000 for bacteria. For multivariateimage visualization, MS-images and optical images were co-registered todefine regions of interest (“ROIs”) for building a supervised trainingmodel. Defined ROIs (classes) were healthy and cancerous tissue for theliver samples and one region for each bacterium plus agar, resultingoverall in 2 classes for liver samples and 4 classes for bacterialsamples.

The training model was used to classify each pixel of the same sampleand colour code the obtained score-values into red-green-blue colourscale. This supervised strategy for image visualization is based on analgorithm that combines recursive maximum margin criterion (“RMMC”) withlinear discriminant analysis (“LDA”). For unsupervised analysis,principal component analysis (“PCA”) was performed on the mass spectradefined by the regions of interest.

Concordance correlation coefficients were used to measure the agreementbetween REIMS imaging platform (“RIP”) mass spectra and iKnife massspectra. This quantitative measure is defined as:

$\begin{matrix}{\rho_{c} = \frac{2{\rho\sigma}_{RIP}\sigma_{iKnife}}{\sigma_{RIP}^{2} + \sigma_{iKnife}^{2} + \left( {\mu_{RIP} - \mu_{iKnife}} \right)^{2}}} & (1)\end{matrix}$

wherein ρ_(c) is the concordance correlation coefficient, ρ is Pearson'scorrelation coefficient and σ_(RIP/iKnife) is the standard deviation ofthe mean intensity values of μ_(RIP/iKnife).

A low concordance correlation coefficient close to the value of zeroindicates low agreement while a value close to the value of one suggestshigh similarity between spectral profiles.

Boxplots show the median at the central mark within the box with 25^(th)and 75^(th) percentiles at the edges of the box. The upper and lowerwhiskers account for approximately 2.7 standard deviations (99.3% datacoverage). Mass spectra were standardized to 100% intensity scale beforetheir data was visualized with boxplots.

FIG. 4 shows in further detail a REIMS imaging platform which comprisesthree major functional elements that all influence the quality of massspectra. The imaging platform comprises a power generator, a xyz-stagewith a sampling probe and a mass spectrometer.

The power supply setup used for the platform comprises a Tektronix® AFG3022 arbitrary function generator (Tektronix®, USA), a Tektronix® DPO3014 Oscilloscope and a Trek 10/40A High Voltage Amplifier (Trek®, USA).

The arbitrary function generator was used to generate sinus waveformswith amplitudes between about 1 V and 6 V at frequencies in the range ofabout 10 to 60 kHz. The high voltage power amplifier multiplied thevoltage by a factor of about 1000 and supplied the connected samplingprobe with the electric current. The oscilloscope provided feedback toensure correct working parameters.

The xyz-stage comprises a modified Prosolia® 2D DESI stage includingFlowprobe® upgrade (Prosolia®, USA) with a high precision z-axisactuator. The sampling probe is mounted onto the actuator and isconnected to the power generator setup as well as a MS inlet capillary(as shown in FIG. 5 ).

A laser height sensor may be provided to measure the distance betweenthe electrosurgical tip and the sample surface and ensures an equalpenetration depth of the tip into the sample which is particularlyuseful for an uneven sample surface. The electrosurgical tip can beexchanged for other materials or shapes depending on the field ofapplication. In case of high precision sampling, a small diameter wiremay be used, whereas a large surface tip is suitable to maximize massspectrometric signal intensity. The electrosurgical tip is surrounded bya tubing which is connected to a Venturi air jet pump.

Bacterial Identification/Imaging

Tissue ion imaging has been described above to assist in theunderstanding of ion imaging of microbial populations.

REIMS imaging analysis of bacteria was carried out at about 1 barVenturi gas pressure, about 2 kV, about 40 kHz AC, with a blade-shapedelectrosurgical tip, about 1 mm pixel size, about 0.1 s time remaininginside the sample and about 1 mm pointing depth.

Bacterial strains of P. aeruginosa ATCC 27853, B. subtilis ATCC 6633 andS. aureus ATCC 25923 were cultured in a single petri dish on solidagar-based media (Oxoid®, U.K.). Incubation was carried out underatmospheric conditions at 37° C. overnight. REIMS analysis was carriedout directly from solid culture medium on the Waters Xevo G2-S® massspectrometer. Peak identifications were carried out on isolated strainsusing tandem mass spectrometry and the REIMS bipolar forceps approach.

Imaging mass spectrometric techniques such as MALDI-MSI and(nano-)DESI-MSI are increasingly applied in microbiological context asthey offer the unique opportunity to study the spatially-resolveddistribution of metabolites in a microbial colony. Additionally,microbial cultures cannot only be studied individually but theinteractions of different microorganisms can be analysed directly and inmany cases in vivo in 2D and after sectioning of the growth medium in3D. This can reveal novel insights into defence mechanisms of certaintypes of bacteria and can be extended to the imaging of microbialinfections and the study of microbe-host interactions.

REIMS imaging analysis of the bacterial strains P. aeruginosa, B.subtilis and S. aureus was carried out directly from the coloniesgrowing on agar plates in vivo. The detected spectrometric species showhigh resemblance with those of the same strains obtained using bipolarREIMS. The mass spectra are each dominated by intact phospholipidspecies in the mass range of m/z 600 to 1000, identified as phosphatidicacid (“PA”), phosphatidyl-glycerol (“PG”) and phosphatidyl-ethanolamine(“PE”) species. Fatty acids are mostly present in the lower mass range,whereas cardiolipins give strong signal in the higher mass range (seeFIG. 6 ). Using the mass range of m/z 400 to 2000, all three strains aredistinguishable from each other using both supervised and unsupervisedmultivariate methods (see FIGS. 7 and 8 ). The multivariate images showdistinct separation of each of the three species, with agar not groupinginto any of the three strains.

Unlike in bipolar REIMS, where the agar surface remains intact, with amonopolar REIMS imaging setup the sampling probe is directly immersedinto the agar culturing medium during analysis. However, the ion yieldfrom agar was generally low, devoid of all the lipid peaks observed inbacteria. This low ion yield might be associated with thecarbohydrate-based agar matrix undergoing condensation reactions bylosing water, resulting in charring and therefore hindering ionformation via the REIMS mechanism.

Single ion images reveal the spatial distribution of excretedmetabolites such as the lipopeptide surfactin in B. subtilis. Surfactinwas reported to exhibit antibacterial, antiviral and antifungalproperties. The surfactin signal was equally distributed over the B.subtilis culture. However, excretion of surfactin into neighbouringareas not directly inhabited by B. subtilis can be observed in FIG. 7 .

In the case of Pseudomonas aeruginosa, a range of PQS-derived quorumsensing molecules were observed with similar distributions to eachother.

While structural cell membrane components such as PA(34:1) are equallydistributed over the whole area covered by P. aeruginosa, theextracellular quorum-sensing metabolites are found in significantlyhigher abundance on the outer edge of the P. aeruginosa growth area asvisualized for PQS (Pseudomonas quorum signal,2-Heptyl-3-hydroxy-4(1H)-quinolone) in FIG. 9 .

The area with high concentration of quorum-sensing molecules seems tocorrelate to the P. aeruginosa bacterial cells that were swarming fromthe main growth area. Quorum sensing molecules such as PQS are excretedby a wide variety of bacteria for both cell-to-cell communication withinthe same or between bacterial species. Quorum-sensing has been relatedto a wide variety of behaviours in P. aeruginosa including swarming andbiofilm production. A comparison of the mean intensity levels of thephospholipid classes shows similar relative intensity distributions forPA, PE and PG classes across all bacterial strains (see FIG. 9 ).Cumulative intensity of PA ion species is slightly elevated compared tothe other classes, being approximately 5% higher in intensity comparedto PG class for P. aeruginosa and S. aureus and about 15% higher for B.subtilis.

The results demonstrate successful multivariate differentiation andidentification of endogenous and exogenous bacterial species, whilesimultaneously allowing the spatially-resolved localization of metabolicfeatures, eventually giving information on biochemical pathways andinteractions between microbial species. A REIMS-based imaging platformadditionally marks the first step towards an automated sampling systemfor microbial cultures for colony-to-colony sampling on a platecontaining multiple organisms.

The automated nature of the REIMS imaging platform enables thesystematic collection of reference mass spectra for use in spectrallibraries necessary for classification of unknown tissue or bacteria. Inboth cases, REIMS imaging technology was able to clearly distinguishbetween healthy/cancerous tissue and between three bacterial strains.This enables the localization of metabolites within the growth area ofbacteria as well as an automated identification system formicroorganisms.

The ability to arbitrarily choose the material and the shape of theelectrode provides a versatile application of the technology dependingon the needs of the user, while the availability of two modes ofsampling (pointing and cutting) adds another layer of flexibility.Principally, any conductive material with biological origin can besystematically analysed without pre-preparation by this technology,enabling a wide range of applications such as, tissue matrix analysis,bacterial identification or food quality management. Since REIMS massspectrometric profiles varies across histological tumour types andbacteria, underlying biochemical information together with a largespectrometric database may provide additional information for futurebiomarker discovery or bacterial pathway exploration.

Analysis Using REIMS Technology

In embodiments disclosed herein, for analysis using REIMS technology,two handheld electrodes in form of a forceps were used as the samplingprobe (bipolar forceps, obtained from Erbe Elektromedizin, Tübingen,Germany). A Valleylab Force EZc power-controlled electrosurgical unit(Covidien, Dublin, Ireland) was used at 60 W power setting in bipolarmode as RF alternating current power supply (470 kHz, sinusoid). Anapproximately 1.5 m long ⅛ in. outer diameter, 1/16 in. inner diameterPTFE tubing (Fluidflon PTFE tubing; LIQUID-scan GmbH Co. KG, Überlingen,Germany) was applied to connect the embedded fluid line of the bipolarforceps and the inlet capillary of either an LTQ Orbitrap Discoveryinstrument (Thermo Scientific GmbH, Bremen, Germany), a Thermo Exactiveinstrument (Thermo Scientific GmbH), or a Xevo G2-S Q-TOF instrument(Waters Corporation, Manchester, UK). In each case the inherent vacuumsystem of the mass spectrometer was used for aspiration of the aerosol.This setup is shown in FIG. 10A-C while instrumental settings are givenin the Table below.

Instrumental parameters of Orbitrap Discovery and Xevo G2-S instrumentsused in this study. Thermo Orbitrap Discovery Exactive Waters Xevo G2-SParameter Setting Setting Parameter Setting Injection time 1000 ms 1000ms Scan time 1000 ms Microscans 1 1 Scan Mode Sensitivity Mass FTMS^(a)FTMS^(b) Mass TOF analyser analyser Ion mode negative negative Ion modenegative Mass range 150-2000 150-2000 Mass range 150-2000 Tube Lens −120V −160 V Sampling 30 V Voltage Cone Capillary −40 V −50 V Source 80 VVoltage Offset Skimmer na −24 V Source 150° C. Voltage TemperatureCapillary 250° C. 250° C. Temperature Automatic Off On Gain Control^(a)Orbitrap Discovery instrument is working at a resolution of 30,000at m/z = 400, ^(b)Mass analyser was used at a resolution of 50,000 (m/z= 200)Mass spectrometric analysis of the microorganisms was typicallyperformed directly from the solid culture medium, in which case about0.1-1.5 mg of microbial biomass was scraped off the agar surface usingone of the electrodes of the bipolar forceps. The two electrodes weresubsequently brought into close proximity (i.e. by pinching the biomassbetween the tips of the forceps) and the RF power supply was triggeredusing a foot switch. The microbial biomass is rapidly heated up due toits non-zero impedance and an aerosol containing the analytes isproduced and transferred directly into the mass spectrometer. Wherepossible, five individual measurements were performed for each strainand averaged as a database entry.

Culturing of Microorganisms

All clinical isolates analysed in some embodiments disclosed herein wereroutinely isolated during clinical microbiology work by trained NHSstaff. Most of the microorganisms analysed during this study werepreviously isolated from blood cultures, identified using a BrukerBiotyper instrument, and stored on beads in a −80° C. freezer. For REIMSanalysis, microorganisms were, for example, grown on a range of solidagar-based media commonly used in clinical microbiology settings. Mediawere purchased from Oxoid (Basingstoke, UK) or E&O Laboratories Ltd.(Bonnybridge, UK). The bacteria were incubated under appropriateatmospheric conditions at 37° C. overnight before analysis. Atmosphericconditions included aerobic (hot room), anaerobic (incubator),microaerophilic (jar in hot room), and aerobic containing 5% CO₂(humidified incubator). Microaerophilic conditions were generated usinga Whitley Jar Gassing System (Don Whitley Scientific Ltd., Shipley, UK).

Analysis of Bacteria Using REIMS Technology

When applying the method provided herein using REIMS technology tobacteria, the majority of phospholipid species detected can be ascribedto PAs, PEs and PGs. This can clearly be seen in Table 1, which showsthe qualitative phospholipid distribution as obtained for nine differentbacterial pathogenic species using exact mass measurements and tandemmass spectrometry measurements acquired during REIMS technologymeasurements. Only high abundance signals (>5% relative abundance) wereincluded. Distinct peak patterns can be obtained for all bacterialspecies, even for those that are closely related such as differentStreptococcus spp. or members of the Enterobactereaceae family (E. coli,C. koseri, K. pneumoniae, S. marcescens, P. mirabilis). Most spectralpatterns for both Gram-negative and Gram-positive species are seen to bedominated by high abundance quasi-molecular PG signals.

Generally, Gram-negative species display a higher amount of unsaturatedphospholipid species and a higher relative amount of PEs. This is ingood agreement with literature published about the bacterialphospholipid composition. Staphylococcus aureus (and otherStaphylococcus spp.) are clearly distinguished from other bacterialspecies by the fact that they exclusively show signals arising fromsaturated phospholipid species.

Metabolite Identification

Bacterial metabolites were primarily identified based on exact massmeasurements and literature references on compounds with the same exactmass that were found in the same bacterial species. Mass deviations werecalculated using the following formula

${\Delta{m\lbrack{ppm}\rbrack}} = {❘{\frac{m_{\exp} - m_{th}}{m_{th}}10^{6}}❘}$withΔm = massdeviation(inppm)m_(exp) = experimentalexactmass, 4decimalplacesaccuracym_(th) = theoreticalexactmass, 4decimalplacesaccuracy

Mass accuracies of <3 ppm were regarded to be confirming the proposedthe sum formula. Further structural identifications were only made byadditional literature references, confirmed by additional tandem massspectrometry measurements if signal intensity was found sufficientlyhigh and reference spectra were available. Fragmentation experimentswere performed on either on a Thermo LTQ XL or Xevo G2-S instrument andusing collision induced dissociation as fragmentation mechanism.

Distinction Between Bacteria and Yeast

There are marked differences in the phospholipid composition betweenbacteria and fungi. The inventors have determined that REIMS spectralprofiles of bacteria differ extensively between different bacteria,whereas the REIMS spectra of fungi have an overall very conservedappearance with differences largely arising from different phospholipidratios, rather than the presence or absence of certain lipid species.Thus, the method provided herein was successfully used to distinguishbetween bacteria and fungi.

Thus, optionally, the method may be used to detect the presence orabsence of bacteria in a sample. Optionally, the method may be used todetect the presence or absence of fungi in a sample. Optionally, themethod may be used to determine whether a microbe is a bacterium or afungus. Optionally, the method may be used to detect the presence ofbacterial contamination in a non-bacterial culture, such as, a fungalculture or an animal cell line culture. Optionally, the method may beused to detect the presence of fungal contamination in a non-fungalculture, such as, a bacterial culture or an animal cell line culture.

Candida Speciation

Candida species are found within the environment, soil and on surfaces.They can cause a range of infections from thrush to sepsis, and be aproblem, e.g., in immune-compromised patients such as those sufferingfrom HIV or cystic fibrosis. In the UK they are the 9th most commoncause of bloodstream infections and 90% of these are due to C. albicans.It is clinically useful to speciate Candida species because Candidaspecies other than C. albicans are typically more drug resistant and areoften intrinsically resistant to azole antifungals.

Identification of yeasts using MALDI TOF MS requires the pre-treatmentof the yeast sample prior to mass spectrometric analysis in order togive reliable identification performances (score >2.0). While therecommended sample pre-treatment for MALDI TOF MS comprises the completeextraction of the fungal material using formic acid and acetonitrile,intact yeast species can directly be analysed by the method providedherein without any modifications in experimental setup or analysisworkflow.

Seven different Candida species were sampled and examined using theforceps method and REIMS.

As shown in FIG. 26 , it was possible to distinguish all species. Leaveone out cross validation scores were 100%. In this Example, thespectrometric data of any of the tested Candida species may serve as a“comparator” spectrometric data with respect of each of the other testedspecies. Alternatively or in addition, the spectrometric data of any ofthese species may be compared to “reference” spectrometric data of oneor more known Candida species.

Thus, optionally, the method may be used to detect, identify and/orcharacterise one or more Candida species. Optionally, the method may beused to detect or confirm the presence or absence of C. albicans in asample, for example by comparing the spectrometric data from the sampleto a reference spectrometric data of C. albicans.

Optionally, the method may be used to detect or confirm the presence orabsence of one or more Candida species selected from C. albicans, C.galbrata, C. krusei, C. guilliermondii, C. lusitaniae, C. parapsilosisand/or C. tropicalis.

Optionally, the method may be used to detect or confirm the presence orabsence of one or more Candida species selected from those listedelsewhere herein.

The sample may optionally be known to contain at least one yeastspecies, e.g., one Candida species.

Analysis of Microbial Mixtures

To determine whether species specific peaks could be observed from mixedcultures, known quantities of bacteria were amalgamated and analysedusing forceps based REIMS. For example, as shown in FIGS. 27A-C, when 10μl of E. coli and C. albicans were mixed together and analysed usingREIMS, species specific peaks could be observed within the mixedspectra.

FIG. 27A shows a mass spectrum of Escherichia coli, FIG. 27B shows amass spectrum of Candida albicans and FIG. 27C shows a mass spectrum ofa mixture of Escherichia coli and Candida albicans.

Thus, in one embodiment, the method provided herein may be used todetect, identify and/or characterise a sample comprising a microbialmixture. By “microbial mixture” is meant that at least 2 differentmicrobes are present in a sample, so a first and a second microbe may bepresent. Optionally, at least 3, 4, 5, 6, 7, 8, 9 or at least 10different microbes are present in the sample. Optionally, the differentmicrobes are taxonomically different, e.g., different strains, species,genera, classes or the like. In another embodiment, the differentmicrobes differ at least in one characteristic, such as drug sensitivityor the ability to produce a particular compound. Thus, optionally, thedifferent microbes may be identical or different at a taxomonic levelsuch as Gram stain, class, family, genus, species and/or strain.

Optionally, the method may be used to detect, identify and/orcharacterise 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10, or at least 1, at least 2,at least 3, at least 4, at least 5, at least 6, at least 7, at least 8,at least 9 or at least 10 of the microbes present in a sample comprisinga microbial mixture.

Optionally, the method provided herein may be used to confirm thepresence or absence of E. coli and/or C. albicans in a sample.Optionally, the sample may comprise a microbial mixture.

Subtyping of Microbes

Microbial typing provides information on the genetic relationshipsbetween strains. This process is critical for tracking the spread ofinfectious diseases, informing infection control practices and, in someinstances, providing useful information about the nature of the microbe,for example whether it is a highly pathogenic variant. The suitabilityof the method provided herein using REIMS technology to provide accuratestrain level discrimination was shown using various examples asdiscussed below.

Ribotyping of C. difficile

Clostridium difficile is a Gram-positive anaerobic bacterium and itsderived infections are often nosocomially (i.e. in a hospital) acquiredinfections obtained after broad-band antibiotic treatment, which allowsexcessive growth of this more hardy and spore-forming species.Clostridium difficile is an important cause of antibiotic associateddiarrhoea and has a case fatality rate of up to 30%. Typing informationis used clinically to understand the epidemiology of disease and todetermine whether an isolate has been transferred from one patient toanother. In routine clinical microbiology, severe C. difficile outbreaksare often associated with certain ribotypes such as ribotype 027 or 078which are thought to be especially pathogenic. Therefore, it isespecially interesting for clinical microbiology labs to establishwhether an infection was acquired during the hospital stay(nosocomial—all patients would be expected to have been infected by astrain of the same ribotype) or whether the infection was caused by astrain acquired before entering the hospital (different patients may beexpected to be infected by strains of different ribotypes).

Ribotyping of C. difficile is routinely performed by isolating C.difficile on specific media, such as Braziers medium, and subsequentlyperforming PCR amplification of the 16S-23 S intergenic spacer region todetermine the ribotype pattern. This process is very time consuming andlabour intensive, so the specificity of the REIMS technique wasinvestigated for this particular problem.

As C. difficile sporulates in adverse conditions, this may affect thecell membrane lipids in turn affecting spectral profiles. Cultureconditions should be standardised to reduce any confounding factors thatmay introduce differences in the spectral patterns.

10 strains of each of three different ribotypes of C. difficile werecultured on Columbia blood agar for 24 hrs under anaerobic conditions.The ribotypes included 002 and 014 which are thought to be lesspathogenic and the more pathogenic ribotype 078. Clear separation trendscan be observed (see FIG. 28 ). Overall cross-validation accuracy was90%. Thus, the method provided herein using REIMS can provide strainlevel information.

Typing of Pseudomonas aeruginosa

Pseudomonas aeruginosa (P. aeruginosa) is an organism that whileubiquitous and generally not pathogenic, can cause severe infectionsincluding sepsis and pneumonia. It is also a significant pathogen forCystic Fibrosis (“CF”) patients where it can lead to exacerbations.Currently P. aeruginosa isolates are commonly typed by Variable NumberTandem Repeat (VNTR) testing, e.g., at the Public Health Englandreference laboratory.

The method provided herein using REIMS was successfully used todistinguish between two different P. aeruginosa strains obtained from CFpatients (data not shown).

Typing of Escherichia coli

The method provided herein using REIMS was successfully used todistinguish between two different E. coli strains: OP50, derived fromparent strain B, and C600 derived from parent strain K-12 (data notshown).

Serotyping of Streptococcus pneumoniae

Streptococcus pneumoniae is a Gram-positive bacterium that causes avariety of infectious diseases in children and adults, includingbacteremia, meningitis and infections of the respiratory tract. Youngchildren and the elderly are most affected and it is estimated thatabout one million children die of pneumococcal disease every year,especially in developing parts of the world. Streptococcus pneumoniaecells are covered with layers of polysaccharides forming a capsule whichis an essential factor in virulence. 91 distinct pneumococcal serotypeshave been identified, however, only a comparably small number of theseserotypes are accounting for most diseases in infants. Identification ofS. pneumoniae serotypes is most commonly performed using the Quellungreaction which involves adding an antibody solution to a broth of S.pneumoniae and observing a positive reaction indicated by “swelling” ofthe bacterial cells. This test is laborious and time-consuming andconsists of a range of subsequent individual tests until a serotype isunambiguously identified. Usually antibody solutions are added inmixtures of several antibodies at a time to reduce amount of testsnecessary. Molecular serotyping methods involving PCR are ratherexpensive and need extensive sample processing. Therefore, astraightforward way to distinguish between different pneumococcalserotypes without the need to introduce further sample processing stepsbesides those needed for species-level identification would have a hugeimpact on daily microbiological practice.

The method provided herein using REIMS was successfully used todistinguish between two different Streptococcus pneumonia serotypes,serotype 14 and serotype 3 (data not shown).

Thus, in one embodiment, the method provided herein may be used formicrobial typing, such as strain typing, ribotyping and/or serotyping,optionally C. difficile ribotyping Streptococcus pneumonia serotyping,typing of E. coli and/or typing of P. aeruginosa strains.

Conventionally, ribotyping is the characterization or classification ofbacteria on the basis of their rRNA gene sequences. It can be done,e.g., by 16S rRNA gene PCR-RFLP and sequencing. Optionally, the methodprovided herein may be used to analyse, e.g., whether a microbe has aparticular ribotype, to distinguish between 2 or more microbes havingdifferent ribotypes, to detect a microbe having a particular ribotype,and the like.

Conventionally, serotyping is the characterization or classification ofmicrobes on the basis of particular surface structures. Optionally, themethod provided herein may be used to analyse, e.g., whether a microbehas a particular serotype, to distinguish between 2 or more microbeshaving different serotypes, to detect a microbe having a particularserotype, and the like.

Analysis of Inter-Species Variance Versus Intra-Species Variance

The general requirement that needs to be fulfilled for a methodology toserve as a general identification tool is that the inter-speciesvariance is larger than intra-species variance. The biological varianceintroduced to the overall spectral appearance by different strains ofthe same species was assessed and the results can be seen in FIG. 30 ,for the three most common and most extensively studied pathogenicspecies encountered in clinical microbiology, namely Pseudomonasaeruginosa, Staphylococcus aureus and Escherichia coli.

FIG. 30 shows REIMS spectral profiles as obtained for five differentclinical isolates of the respective species. Excellent pattern stabilitycan be observed for S. aureus. For E. coli, some changes in relativesignal intensity can be observed between major phosphatidylglycerolphospholipid species in the mass range m/z 600-800. A very high degreeof similarity is observed for the high mass range. In case of P.aeruginosa, three of five strains exhibit production of extracellularmetabolites such as quorum-sensing molecules and rhamnolipids in themass range of m/z 200-350 and m/z 500-680, respectively. Identicalstrains furthermore exhibit a group of signals in the range of m/z900-1100. However, the phospholipid region between m/z 680-800 andmasses above m/z 1100 show good agreement between all five clinicalisolates of P. aeruginosa.

In all of these cases it is apparent that large parts of the spectralinformation remains conserved, especially for masses above m/z 1100.This highly conserved nature of signals at m/z >1000 has been largelyignored by other lipidomics technologies in the past, which might be dueto difficulties in detection for technologies such as DESI.

Thus, in one embodiment, the method provided herein may be used todetect, identify and/or characterise a microbe selected fromPseudomonas, Staphylococcus and/or Escherichia, optionally Pseudomonasaeruginosa, Staphylococcus aureus and/or Escherichia coli.

A further data set was created comprising 28 different bacterial speciesrepresented by 15 different clinical isolates each as detailed infollowing table:

Gram-stain Family Genus Species Growth conditions negativePseudomonadaceae Pseudomonas aeruginosa CBA, aerobic EnterobacteriaceaeCitrobacter koseri CBA, aerobic Enterobacter aerogenes CBA, aerobiccloacae CBA, aerobic Klebsiella oxytoca CBA, aerobic pneumoniae CBA,aerobic Escherichia coli CBA, aerobic Proteus mirabilis CBA, aerobicMorganella morganii CBA, aerobic Serratia marcescens CBA, aerobicPasteurellaceae Haemophilus influenzae CHOC, aerobic (5% CO₂)Burkholderiaceae Burkholderia cepacia complex CBA, aerobic (5% CO₂)Xanthomonadaceae Stenotrophomonas maltophilia CBA, aerobicBacteroidaceae Bacteroides fragilis CBA, anaerobic MoraxellaceaeMoraxella catarrhalis CBA, aerobic Neisseriaceae Neisseria gonorrhoeaeCBA, aerobic (5% CO₂) positive Staphylococcaceae Staphylococcus aureusCBA, aerobic epidermidis CBA, aerobic capitis CBA, aerobic haemolyticusCBA, aerobic hominis CBA, aerobic Enterococcaceae Enterococcus faecalisCBA, aerobic (5% CO₂) faecium CBA, aerobic (5% CO₂) ClostridiaceaeClostidium difficile CBA, anaerobic Micrococcaceae Micrococcus luteusCBA, aerobic Streptococcaceae Streptococcus agalactiae CBA, aerobic(5%CO₂) pyogenes CBA, aerobic (5% CO₂) pneumoniae CBA, aerobic (5% CO₂)

The generated datasets were analysed using supervised and unsupervisedanalysis. Generally the plots resulting from the supervised andunsupervised analysis of REIMS technology data showed high similarity toeach other. This is due to the fact that REIMS technology featuresexclusively signals originating from the sample (i.e. not from anychemical background). This is an advantage compared to MALDI MS wherematrix related signals significantly contribute to the overall spectralinformation.

The results demonstrate that REIMS technology spectral profiles largelyfollow taxonomical trends. Cross-validation revealed 95.9%, 97.8% and100% correct classification at species-, genus- and Gram-level.

Gram-positive and Gram-negative species were found to separate along thefirst multivariate component in both PCA and RMMC analysis. Compared toGram-negative species, Gram-positive bacteria generally showed a higheramount of saturated phospholipid species and lower relative abundance ofphosphatidylethanolamines. These observations are in agreement with thebacterial cell membrane composition reported in the literature.Hierarchical cluster analysis was performed in order to investigate howwell the REIMS spectral profiles follow the bacterial taxonomy asdetermined by 16S rRNA gene sequences. Hierarchical Cluster Analysis(HCA) was performed using Euclidean pairwise distance calculation with acomplete linkage metric. 3×3 strains of the original dataset wereaveraged for each bacterial species to form the dataset which was thensubjected to HCA. This step was undertaken in order to facilitatevisualization while still incorporating a maximum of the biologicalvariance among strains of a certain species. The inventor found thatspectral profiles of closely related bacterial species were groupedclosely together while rather unrelated bacterial species groupedseparately. For the Gram-positive species this was determined for eachthe Staphylococcus spp. (S. aureus, S. capitis, S. epidermidis, S.hominis and S. haemolyticus), Streptococcus spp. (S. agalactiae, S.pneumoniae, S. pyogenes) and two Enterococcus spp. (E. faecalis and E.faecium). Streptococcus and Enterococcus spp. which both belong to theLactobacillales order were further situated on the same cluster in theHCA.

Regarding Gram-negative species, all members of the Enterobacteriaceaefamily (members of the genera Escherichia, Citrobacter, Enterobacter,Proteus, Morganella, Klebsiella and Serratia) grouped closely together.Furthermore, Pseudomonas aeruginosa, Moraxella catarrhalis andBurkholderia cepacia complex strains were all located together in aseparate cluster when compared to the other Gram-negative species.Pseudomonas spp. and Moraxella spp. are both part of the Pseudomonadalesorder. Although Burkholderia cepacia complex strains belong to□-Proteobacteria today, they were previously classified into thePseudomonas genus, thus indicating a high phenotypic similarity betweenPseudomonas spp. and Burkholderia spp. which explains their proximity onthe HCA dendrogram. The same trends were observed in PCA plots forGram-positive and Gram-negative-species only. Similarly to HCA, theseresults demonstrate that REIMS technology spectral profiles largelyfollow taxonomical trends.

Effect of Growth Conditions

Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli areall facultative anaerobes and can thus survive and grow under a varietyof different atmospheres. REIMS technology profiles were obtained afterculturing the bacteria under the four most commonly used atmospheres.For P. aeruginosa and E. coli the different culture conditions did notresult in any significant differences in the spectrometric data (datanot shown).

FIG. 31A shows zoomed regions of mass spectra of S. aureus grown underaerobic conditions in the mass range m/z 650-800, FIG. 31B shows zoomedregions of mass spectra of S. aureus grown under anaerobic conditions inthe mass range m/z 650-800, FIG. 31C shows zoomed regions of massspectra of S. aureus grown under aerobic conditions in the mass rangem/z 1250-1750 and FIG. 31D shows zoomed regions of mass spectra of S.aureus grown under anaerobic conditions in the mass range m/z 1250-1750.

Significant changes can be observed in the spectral profile of S. aureuswith a strong shift of phosphatidylglycerols expression towards lipidspecies with longer chain lengths, both for phosphatidylglycerols at m/z650-800 as for higher mass species in the range of m/z 1300-1700, asshown in the zoomed spectrometric regions in FIGS. 31A-D.

Similar results were obtained in a study analysing the fatty acidcomposition of S. aureus grown under aerobic and anaerobic conditionsusing gas-liquid chromatography. While under aerobic conditions, fattyacids with 15, 18, and 20 carbon atoms account for 40.86%, 3.7%, and21.84% of the overall fatty acid content, under anaerobic conditionsthese numbers change to 16.26%, 22.38% and 37.65%, respectively (D. C.White, F. E. Frerman, Fatty Acid Composition of the Complex Lipids ofStaphylococcus aureus During the Formation of the Membrane-boundElectron Transport System. Journal of Bacteriology 95, 2198-2209(1968)). This was associated with the membrane-bound electron transportsystem being inactive under anaerobic conditions. As S. aureus was foundto contain less than 1% free fatty acids, these fatty acids arepredominantly built into membrane lipid species and thus leads to asignificant increase in heaver phospholipid species which can beconfirmed using the spectrometric data obtained via the method hereinusing REIMS technology.

The effect of different media on bacteria was determined by growing tendistinct isolates of Staphylococcus epidermidis on three commonly usedagars; aztreonam, blood and chocolate agar. The agar type did not affectthe lipid profiles (data not shown).

Mycolic Acids in Corynebacterinaceae Suborder

The suborder Corynebacterineae forms a large group of actinomycetespecies characterized by the presence of mycolic acids. Mycolic acidslong fatty acids that are generally composed of a longer beta-hydroxychain (meromycolate chain) and shorter alpha-alkyl side chain(α-branch). The variability of their chain lengths and the complexity oftheir structures contribute to the definition of the genera, from thesimplest corynomycolic acids of Corynebacterium to the most complexmycolic acids of the Mycobacterium genus. The number of carbon atoms andthe degree of desaturation (refers to the number of double bonds and/orcyclopropane rings) of main and side chains vary according to the genusconsidered. Using the method provided herein with REIMS technology,spectral profiles were obtained for members of the four most commongenera of the Corynebacterineae suborder as encountered in clinicalmicrobiology. The inventors determined that Corynebacterium aftermentansdisplays mycolic acids of chain lengths ranging from C28 to C36; whereasRhodococcus equi displays mycolic acids of chain lengths ranging fromC28 to C39; Nocardia ateroides displays mycolic acids of chain lengthsranging from C48 to C60; and Mycobacterium avium displays mycolic acidsof chain lengths ranging from C77 to C81.

Mycolic acids constituents were determined for Rhodococcus equi ATCC6939. Rhodococcus spp. are reportedly known to contain mycolic acidsbetween 30-54 carbon chain lengths and 0-2 unsaturations. For theanalysed Rhodococcus strain, mycolic acids with overall chain length of28-39 were found with 0-3 unsaturations. The overall chain length forthis particular strain is in good agreement for R. equi species asreported in literature. However, a higher amount of unsaturations werefound in the presented study (see Table 4). This might be due to a lackof sensitivity in the literature source, i.e. due to a highersensitivity of the method provided herein compared to conventionalmethods, because the highly unsaturated species detected in the REIMStechnology spectra are of low spectral intensity.

Nocardia spp are reportedly known to contain mycolic acids with chainlengths between C48-60 with a degree of unsaturation between 0-3. Thesefindings could be confirmed using the method provided herein for aNocardia sp. isolated from a sample of respiratory origin andtype-strain Nocardia asteroides ATCC 19247. Detected mycolic acidspecies are listed in Table 5.

Strains of Mycobacterium avium, M. fortuitum and M. peregrium wereobtained from clinical respiratory specimens and analysed using themethod herein using REIMS technology. The detected mycolic acid speciesare listed in Table 6. Mycobacteria are reportedly known to containmycolic acids with chain lengths between 60-90 carbons and 1-2unsaturations. While other members of the Corynebacterineae subordermainly contain unfunctionalised mycolic acids, there is a largerstructural variability in Mycobacteria ranging from unfunctionalisedalpha-mycolic acids to methoxy-, keto- and epoxy-functionalities as partof the beta-hydroxy side chain. Apart from Segniliparus spp., no othermember of the Corynebacterineae suborder contains similarly long mycolicacids. However, while lower chain length mycolic acids were found withhigh intensity, mycolic acids in case of Mycobacterium spp. were foundwith comparably low spectral intensity. This might either be due tolower general abundance in the membrane of Mycobacteria as compared toother genera or more likely decreasing ionisation efficiency withincreasing mycolic acid chain length.

Sphingolipids in Bacteroidetes Class

Sphingolipid production is ubiquitous among eukaryotes but present inonly a few bacterial genera. Species of Bacteroidetes, a Gram-negativebacterial phylum whose members often comprise around 50% of the humangut community, are unusual in that they produce sphingolipids with up to40-70% of their total membrane phospholipid content.

Sphingolipids are signaling molecules that play a key role in modulatingthe host immune response. Genera that are comprised within theBacteroidetes phylum include Bacteroides, Parabacteroides, Prevotella,Tannerella and Porphyromonas spp.

The method provided herein using REIMS technology was used to analysemembers of the Bacteroidetes phylum. Spingolipids that were described inliterature and identified in spectra generated via this method includefree ceramides, phosphoethanolamine dihydroceramides, as well assubstituted and unsubstituted phosphoglycerol dihydroceramides. Anoverview of these compounds is given in Table 7, wherein a) denotes: seeFragmentation spectra in FIG. 13 ; and b) denotes: see fragmentationspectra in FIG. 14 .

Ceramides were not found to be identical with those observed for samplesof mammalian origin but were found to contain an additional oxygenmolecule. A fragmentation mechanism of the [M+Cl]⁻ ion at m/z 590 isshown in FIG. 13 explaining the two major fragments observed fromcollision induced dissociation of the parent ion. FIG. 14 showsfragmentation spectra obtained for C15:0 substituted phosphoglyceroldihydroceramides that were produced by members of the Parabacteroidesgenus. Main fragments at m/z 153 and 171 can be ascribed to theglycerolphosphate headgroup while m/z 241 can be attributed to C15:0acyl chains. A neutral loss of 242 Da form the parent ion canadditionally be ascribed to the C15:0 acyl chain. These compounds havebeen described for Porphyromonas gingivalis (not part of the presentdatabase) and thus seem specific for the Porphyromonadaceae family.These compounds were furthermore reported to penetrate into humantissues and were found in blood, vasculature tissues and brain.

It was recently reported that Bacteroides fragilis NCTC 9343additionally produces an isoform of α-galactosylceramides, asponge-derived sphingolipid that serves as ligand for the host immunereceptor CD1d. The inventor determined that these compounds can be foundin REIMS technology spectra of B. fragilis strains at m/z 752, 766 and780 cannot be found in any other analysed species within the Bactoidetesclass.

Corynebacterium Speciation

Species identification within the Corynebacterium genus can bechallenging using existing methods. For example, partial 16S rRNAsequencing does not lead to sufficient specificity, so ideally the lesscommonly used full sequence is needed. Corynebacteria typically containmycolic acids, so REIMS may be used to identify Corynebacterium species,or to differentiate between Corynebacterium species, for example, on thebasis of spectrometric data pertaining to mycolic acids.

The method provided herein using REIMS technology was successfully usedto distinguish between five different Corynebacterium species: C.striatum, C. minutissimum, C. imitans, C. diphtheria, C. amycolatum(FIG. 32 ). Most strikingly, Corynebacterium amycolatum, an emergingopportunistic pathogen, can be easily distinguished by its absence ofmycolic acid signals.

Using collision induced dissociation, mycolic acids fragment at the bondbetween the meromycolate chain and the a-branch. While the meromycolatechain forms an aldehyde and leaves the parent ion as a neutral entity,the a-branch of the mycolic acid forms a negatively charged carboxylateion, allowing for structural assignment of the parent ion. Fragment ionsobserved (see Table 3) were in good agreement with those reported inliterature for C. glutamicum and confirm chemical assignment.

Thus, in one embodiment, the method provided herein may be used todetect, identify and/or characterise one or more Corynebacteriumspecies. In one embodiment, the method provided herein may be used todetect or confirm the presence or absence of C. amycolatum in a target,for example by comparing the spectrometric data from said target to areference spectrometric data of C. amycolatum. In one embodiment, themethod provided herein may be used to detect or confirm the presence orabsence of one or more Corynebacterium species selected from C.striatum, C. minutissimum, C. imitans, C. diphtheria, and/or C.amycolatum.

In any of these embodiments, the sample may optionally be known tocontain at least one bacterial species, e.g., one Corynebacteriumspecies.

Antimicrobial Susceptibility Testing (“AST”)

Antibiotic resistance of microbes is a global problem of increasingsignificance that often can significantly complicate treatment ofinfections. The protein profiles that are acquired during routine MALDITOF analysis do not contain information on the antibiotic sensitivityand resistance pattern. As discussed elsewhere herein, culture-basedmethods for testing antibiotic sensitivity are time-consuming.

Staphylococcus aureus can cause a range of infections includingpneumonia, bacteraemia and skin and soft tissue infections. Methicillinresistant Staphylococcus aureus (MRSA) strains are resistant tobeta-lactam antimicrobials and result in increased length of hospitalstays, higher economic costs and poorer clinical outcomes. Moreover, itis a leading cause of Hospital Acquired Infections (HAIs) and isestimated to account for 44% of HAIs in the EU each year. Because MRSAcolonisation has been identified as a major risk factor in thedevelopment of an MRSA infection, and to curb nosocomial spread,universal or targeted screening programmes are often adopted.

30 MRSA and 30 methicillin susceptible S. aureus (MSSA) isolates wereexamined using the method provided herein using REIMS technology. LDAand cross validation analysis (FIG. 29 ) revealed a clear separation ofMSSA and MRSA isolates indicating that the method provided hereinprovides a useful tool for MRSA screening.

The method provided herein using REIMS technology was also successfullyused to distinguish between some antimicrobial-resistant(cabapenemase-producing) and antimicrobial-sensitive (notcabapenemase-producing) strains of Klebsiella pneumonia.

Thus, in one embodiment, the method provided herein may be used todetect, identify or characterise a microbe having sensitivity to anantimicrobial. In one embodiment, the method provided herein may be usedto detect, identify or characterise a microbe having resistance to anantimicrobial. In one embodiment, the method may be used to distinguishbetween antimicrobial-resistant and antimicrobial-sensitive microbes.

Optionally, the antimicrobial may be selected from any of theantimicrobials disclosed elsewhere herein.

Optionally, the antimicrobial-resistant microbe may be selected from aproducer of β-lactamase, such as cabapenemase or TEM-1 β-lactamase; aproducer of chloramphenicol acetyltransferase, a producer of atetracycline efflux system, a producer of AmpC cephalosporinase; and/oran over-producer of DHF (dihydrofolate) reductase.

Optionally, the antimicrobial-resistant microbe may be MRSA and/or theantimicrobial-sensitive microbe may be MSSA.

Analysis of Liquids and Microbial Mixtures

Direct analysis of a liquid such as a microbial culture medium or a bodyfluid is not advisable as evaporation temperatures are restricted by theboiling point of water and thus cannot exceed 100° C. This, togetherwith dilution of cells by the culturing medium considerably limitssensitivity. Thus, the analysis of liquid cultures may optionallyinclude, or be preceded by, a processing step to remove excess liquid toprepare a solid sample. Suitable processing steps, such ascentrifugation and/or filtration, are discussed elsewhere herein.

For most bacterial species, a centrifugation step of 10 mins at 3500 rpmis sufficient to form a cell pellet suitable for REIMS technologyanalysis. However, certain bacterial species such as Klebsiellapneumoniae or Pseudomonas aeruginosa may need to be centrifuged at morerigorous conditions such as 10 mins at 12500 rpm to allow effectiveremoval of the supernatant. The skilled person is aware of, or caneasily determine, which centrifugation conditions are appropriate toallow effective removal of the supernatant. The resulting pellet may beanalysed directly, e.g., using REIMS technology, or it may betransferred onto a solid support such as a swab or a slide. Theinventors have determined that such a transfer does not impact on theanalysis.

In one set of Examples, binary mixtures of bacteria were created atdifferent ratios and analysed using the method provided herein usingREIMS technology.

For this purpose, bacterial biomass was cultured for 48hrs on blood agarand subsequently scraped off using a 10 uL loop and collected in anEppendorf tube for Pseudomonas aeruginosa, Bacteroides fragilis,Staphylococcus aureus and Escherichia coli. Using an analytical balance,bacteria were weighed into a fresh Eppendorf in ratios of 1:5, 1:3, 1:1,3:1, and 5:1 (weight: weight). The method allowed the detection of eachspecies within each mixture.

Thus, optionally, the method provided herein may be used to detect,identify and/or characterise a microbe present in a liquid. Optionally,the method may be used to analyse a liquid to determine whether anymicrobes are present; and/or to identify and/or characterise a microbepresent in said liquid. As explained above, the method optionallyincludes a processing step to prepare a solid sample. Optionally, themethod may be used to analyse a target comprising or consisting of amicrobial mixture, e.g., to detect, identify and or characterise one ormore or all of the different microbes present in said mixture.

Cardiolipins

Cardiolipins (1,3-bis(sn-3′-phosphatidyl)-sn-glycerols) are complexdiphosphatidylglycerol lipids containing four fatty acid chains that candiffer in length and degree of unsaturation. They are predominantlydistributed in bacterial plasma membranes and in eukaryoticmitochondrial inner membranes. Although there is significant potentialfor complexity in cardiolipin structure considering the presence of fourfatty acyl chains, the cardiolipin profiles are generally found tocomparably simple and reproducible. Bacterial cardiolipins predominantlyexhibit shorter carbon chain lengths with mostly saturated ormono-unsaturated fatty acids. However, eukaryotic cells featurepredominantly longer chain polyunsaturated fatty acids as buildingblocks of their cardiolipins. Using the method provided herein with aREIMS technology device, cardiolipins were detected and identified in awide range of both Gram-positive and Gram-negative bacteria, based onexact mass measurements. They were typically detected in the mass rangebetween m/z=1300-1450 which corresponds to overall chain lengths ofCL(60:0)-CL(72:0).

For example, for Staphylococcus epidermidis ATCC 12228, the presence ofcardiolipins was further confirmed using fragmentation patterns. Forthis purpose, the strain was cultured on BHI medium which was found toincrease the relative spectral intensity for cardiolipins as compared toblood agar. The identified cardiolipins species and fragmentationresults are listed in Table 2. In good agreement with thephosphatidylglycerol species in case of the Staphylococcus genus (seeTable 1), only cardiolipins with saturated fatty acyl groups weredetected. This is expected as phosphatidylglycerols moieties form thebuilding blocks of cardiolipins.

Quorum-Sensing Molecules in P. aeruginosa

Cell-to-cell communication via so-called quorum-sensing (QS) mechanismsis ubiquitous in the bacterial world. These systems rely on synthesis ofsmall molecules that diffuse in and out of cells where they promotecollective behaviour by facilitating gene expression. These moleculesare also called autoinducers as among others they also promoteexpression of genes that lead to their own synthesis. QS signalmolecules are also known to cross the procaryote-eucaryote border wherethey may aid bacterial survival by promoting collective advantageousbehaviour. Several chemically distinct molecular families of QSmolecules have been described of which the N-acylhomoserine lactone(AHL) family in Gram-negative bacteria have arguably been best studied.P. aeruginosa is known to employ both AHL and a more unique system whichis linked to the first and which utilises2-heptyl-3-hydroxy-4(1H)-quinolone as main functional entity(Pseudomonas quinolone signal, PQS). PQS and structurally similarderivatives are known to regulate the production of virulencedeterminants such as elastase, rhamnolipids, the galactophilic lectin,LecA, and the pigment pyocyanin. It was further reported to influencebiofilm development and maturation

Using the method provided herein with REIMS technology, abundant signalswere observed in P. aeruginosa that correlate with the PQS-basedquorum-sensing system. These were found in different abundances andcompositions based on culturing medium, culture age, growth mode andstrain. An example for different growth modes observed in a number of P.aeruginosa strains is given in FIG. 34 and shows spectra for cellsacquired from cells of the same strain grown on the same plate in smallsingle colonies (<1 mm diameter) and from cells grown as a lawn. Whileno signals corresponding to quorum-sensing molecules were observed incase of spectra obtained from single colonies, abundant quorum-sensingmolecules were observed for those cells grown in a lawn pattern.Production of quorum-sensing molecules and resulting gene expression isknown to be depending on cell density and was found increased inbiofilms of P. aeruginosa; the observed findings were thus correlatedwith this effect. The same effect was observed in case of rhamnolipidproduction (see below).

Compounds 2-heptylquinoline-4(1H)-one and2-heptyl-3-hydroxy-4(1H)-quinolone (PQS) have been confirmed by theinventor by comparison with tandem mass spectra of standard compounds.Hydroxynonenylquinoline (m/z=268), hydroxynonylquinoline (m/z=270) andhydroxyundecenylquinoline (m/z=296) show similar fragmentation patternsand can thus be ascribed to structurally similar compounds. Commonfragments include m/z 143, 157 and 170. Tandem mass spectra of thesecompounds featured in the literature are only reported for the [M+H]⁺quasi-molecular ion. However, the fragments observed in negative ionmode (m/z=157 and 170) seem to correlate with the fragments observed inpositive ion mode (m/z=159 and 172) and are indicative of4-hydroxy-2-alkylquinolines.(170) Structurally confirmed quorum-sensingmolecules are listed in Table 8. Furthermore, m/z signals at 306 and 332show fragments at m/z 270 and 296 (and a common fragment at m/z 157),respectively (loss of 36 Da) and a isotopic pattern indicative of[M+Cl]⁻ adducts.

Rhamnolipids in P. aeruginosa

Microorganisms like bacteria, yeasts, and fungi are known to producevarious types of biosurfactants. Rhamnolipids are a class ofsurface-active glycolipids containing one or two 3-hydroxy fatty acidsof various lengths, linked to a mono- or dirhamnose (Rha) moiety. Theycan be found as secondary metabolites and in different concentrationsand composition patterns in a variety of Pseudomonas species. Otherbacterial species as some Burkholderia sp. have been reported to producerhamnolipids with longer alkyl chains than those produced by P.aeruginosa. Rhamnolipid production could be linked to physiologicalfunctions such as biofilm formation, uptake and biodegradation of poorlysoluble substrates, surface motility as well as displaying antimicrobialactivity against both Gram-negative and Gram-positive species and arange of fungal species.

Rhamnolipids probably contribute to the inflammatory-related tissuedamage observed in lungs of cystic fibrosis (CF) patients. In fact,rhamnolipid concentrations in CF patients are high: up to 8 μg/mLrhamnolipid concentration was reportedly found in sputum samplesobtained from P. aeruginosa colonised CF patients and as much as 65μg/mL were reportedly found in secretions of a lung removed from a CFpatient.

Although initially described as a mixture of four congeners, thedevelopment of more sensitive analytical techniques has led to thefurther discovery of about 60 rhamnolipids homologues.

The method provided herein was used with REIMS to analyse rhamnolipidproduction by Pseudomonas aeruginosa isolates. Identified rhamnolipidspecies are listed in Table 9. The inventor determined that overallspectral intensity of rhamnolipids depends on a number of factors:single colonies were observed to display less rhamnolipids than whengrown as a lawn (this effect was correlated with the detection andproduction of quorum-sensing molecules, see previous point). Rhamnolipidintensity was also seen to increase significantly with increasing age ofthe culture. However, not all P. aeruginosa strains analysed wereobserved to produce rhamnolipids. Rhamnolipid production is linked tothe quorum-sensing apparatus, a fact that can be observed in REIMSspectra of P. aeruginosa as well where high abundance of rhamnolipidsare usually accompanied by abundant quorum-sensing signals.

Fragmentation spectra of rhamnolipids species listed in Table 9 can beseen in FIG. 35 . Rhamnolipids at m/z=503 and m/z=649 were confirmed bycomparison to tandem mass spectra published in literature. Otherrhamnolipids were assigned based on similar fragmentation patterns suchas the loss of rhamnose moieties and the loss of one of the acyl chainsand exact mass measurements.

Rhamnolipids have several potential biotechnological applications,especially as biodegradable surfactants for use in industry or medicineor as a possible alternative for production of rhamnose. Therefore, ascreening method according to the present invention may optionally beapplied for the identification of optimal rhamnolipid producers.

Lipopeptides in Bacillus Species

Bacillus subtilis is an important Gram-positive model organism thatproduces a variety of antibiotics of which the most important one issurfactin, a cyclic lipoheptapeptide(C₁₃H₂₇HCO|CH₂CO-Glu-Leu-D-Leu-Val-Asp-D-Leu-Leu|, where the symbols ‘|’represent the two cyclic bonding sites. Surfactin and its isomers arecyclic lipopeptide biosurfactants consisting of seven amino acid unitsand one β-hydroxyl fatty acid side chain. The carbon length of the sidechain can range from 13-15 carbons. Due to their hydrophobic sidechains, surfactins can incorporate into the phospholipid bilayer wherethey lead to perturbation of the cells. They showed a variety ofactivities such as anticoagulants and immunosuppressives as well asagainst cancer, viruses, and inflammation. The various polar functionalgroups of the surfactin molecule allow ready ionisation in both positiveand negative ion mode. Other minor related antibiotic compounds producedby B. subtilis include fengycin and iturin, however, these compoundswere not observed in the REIMS spectra, presumably due to concentrationsbelow the limit of detection in the tested strains.

The inventor determined, using the method provided herein with REIMStechnology, that Bacillus subtilis mass spectra are dominated by speciesclustering around m/z=1034 in negative ion mode and m/z=1059 in positiveion mode. These cluster can be ascribed to [Surfactin(C15)-H]⁻ and[Surfactin(C15)+Na]⁺ and C13 and C14 homologues, respectively, as shownin Table 10. The protonated quasi-molecular ions corresponding to [M+H]⁺adducts were not observed in positive ion mode. This is due to the highaffinity of surfactin for sodium cations and high abundance of sodium inall living organisms and the culturing medium. The presence of surfactinand its homologues was further confirmed by tandem mass spectrometrymeasurements. Fragmentation patterns observed in negative ion modecorrespond well with fragmentation patterns reported in literature.

Table 11 provides details of Lichenysin compounds detected using themethod provided herein with REIMS technology in Bacillus licheniformis.Thus, the method provided herein may optionally be used to distinguishbetween various Bacillus species. In particular, between B. subtilis andB. licheniformis. This is difficult to achieve using conventionalmethods.

Polyhydroxyalkanoate Polymers

Polyhydroxyalkanoates or PHAs are linear polyesters produced in natureby bacterial fermentation of sugar or lipids, usually under incombination to a shortage in a non-carbonous nutrient, e.g., nitrogen.They are produced by the bacteria to store carbon and energy. Theseplastics are biodegradeable and are used in the production ofbioplastics. The simplest and most commonly occurring form of PHA is thefermentative production of poly-beta-hydroxybutyrate(poly-3-hydroxybutyrate, P3HB). However, more than 100 differentmonomers have been reported as PHA constituents. Generally, PHAs areclassified into three different classes according to monomer carbonchain length: short (C3-C5), medium (C6-C14) and long (C>14) chain PHAs.More than 90 genera of archae and eubacteria (both Gram-positive andGram-negative) have been reported to produce PHAs.

The inventor detected, using the method provided herein with REIMStechnology, polymers consisting of C₄H₆O₂ (polyhydroxybutyrate)monomers, in different amounts in strains of Bacillus cereu), Delftiaacidovorans, Burkholderia cepacia and Achromobacter xylosoxidans. Theability of the method provided herein with REIMS technology to detectpolymer production in several bacterial species offers an opportunityfor biotechnological applications to rapidly screen organisms forsuccessful production of polymers, average polymer chain lengths andmodifications and/or best polymer producers.

Derivatisation of Taxon-Specific Markers

The general workflow applied for finding taxon-specific markers isdisplayed in FIG. 33 . All data was compiled and a representative subsetof the >4,000 bacterial and fungal strains recorded was generated. Thiswas followed by a peak-picking and peak-matching routine and anANOVA-based biomarker discovery to derive taxon-specific markers.

A large dataset was created comprising 228 different bacterial speciesbelonging to 80 genera, 47 families, 23 orders, 11 classes and 5 phyla.A maximum of five to seven individual strains was used for calculationsfor badly represented genera, while a maximum of 3 strains per specieswas used for well represented genera (10 species or more). Differentculturing media, culturing ages and culturing atmospheres were includedinto the sample set.

For the derivatisation of taxon-specific markers, the data was processedin centroid rather than profile mode. For this, raw mass spectrometricfiles were transcoded to mzXML format by the ProteoWizard msconvert tool(version 3.0.4043).

Peak picking was performed on individual spectra within each separatefile, with processing being performed on each set of m/z values andcorresponding intensity values. Local maxima and peak boundaries aredetermined according to local minima or zero intensity values to eitherside of the maximum. Peak intensity is calculated as its sum between thetwo boundaries, with boundary intensities being halved (important onlyfor un-resolved peaks; resolved peak boundaries are 0). The peak's‘centre of mass’ is determined by means of an intensity-weightedaverage. After processing all spectra of a single mzXML file, theindividual m/z and intensity vectors are aligned using a dynamicprogramming algorithm. The user specifies maximum peak shift (in ppm)that acts as the maximum distance over which peaks can be matched. Ifmultiple files are to be compared, the peak-matching algorithm is re-runusing the same parameters.

Each variable was analysed independently, initially by 1-way analysis ofvariance (ANOVA), to determine if there is a significant differencebetween the means of the defined groups (for example, bacterialspecies). Significant differences (e.g., p<0.05) are further analysed byTukey's honestly significant difference (HSD) post-hoc method. HSDprovides information about confidence intervals and if this intervalspans across zero then the difference between two means is regarded asinsignificant. A difference for each variable between all groups isdetermined. Thus, a variable that differs significantly between one andall other groups is considered a likely biomarker candidate. Results foreach variable are returned as the ANOVA p-value (i.e. is there asignificant difference in group means. NB that it does not identify theactual difference) and the number of significantly different groupmeans. The emphasis has been placed on variables that are significantlyhigher in one group than in others, rather than variables that areabsent in all but one taxonomical group.

For each taxonomical level, both a list of the top 20 most significantpeaks per taxon and 150 most significant peaks in the dataset werecreated and manually compiled into markers that are specific ondifferent taxonomical levels. Mass spectrometric signals weretentatively assigned using exact mass measurements, literature researchand tandem mass spectrometry measurements as obtained during LC/MSexperiments of total lipid extracts or using REIMS on a Waters XevoG2-XS instrument. A list of most specific markers at differenttaxonomical levels was compiled and subsequently these markers werevisualised in colorectal tumour resection samples and gastrointestinalbiopsy samples to test for the presence or absence of these taxa. Asthese imaging data sets were recorded using a mass range of m/z200-1000, the identification of taxon-specific markers was restricted tothe same mass range.

Single ion images and RGB images were generated using MSiReader Version0.05(135) with linear interpolation (order 1) and 0.005 Da bin size.

PCA was applied to initially assess the similarity between REIMSspectral profiles and bacterial species on different higher taxonomicallevels such as Gram-, phylum-, class- and order-level.

An overall good separation was obtained for Gram-positive andGram-negative species along the first principal component. Goodtaxonomic markers were also identified for the lower taxa, such asorder, family, genus, species or strain.

For example, m/z=618.5233 (hydroxylated ceramide species, C36H73NO4Cl as[M+Cl]—) is indicative of a member of the Bacteroidales order beingpresent, while m/z=820.7522 is indicative of a member of theBacteroidaceae family. m/z=752.5449, corresponding toα-Galactosylceramide, is indicative of Bacteroides fragilis.

The following examples of biomarkers were identified.

Firstly, mycolic acids for bacteria belonging to the Corynebacterineaesuborder such as Mycobacterium spp., Corynebacterium spp. andRhodococcus spp. In particular, the following mycolic acids have beendetected from the corresponding genera:

-   Mycobacterium spp.: C77-C81 (even and odd numbered, 0-2    unsaturations); Corynebacterium spp.: C28-C36 (even numbered, 0-2    unsaturations);-   Nocardia spp.: C48-056 (even numbered, 0-3 unsaturations);-   Rhodococcus spp.: C28-C38 (even and odd numbered, 0-4    unsaturations).

Secondly, a variety of sphingolipid species were found to be specificfor members of the Bacteroidetes phylum. These sphingolipids includeoxidized ceramides species, phosphoethanolamine dihydroceramides andC15:0-substituted phosphoglycerol dihydroceramides and dihydroceramide.Among those sphingolipid species, a series of galactosylatedsphingolipids was found to be specific for Bacteroides fragilis(Bacteroides fragilis alpha-Galactosylceramides).

Thirdly, among bacteria, plasmalogens are highly specific for anaerobicbacteria such as Clostridium spp. and Fusobacterium spp. This is due tothe fact that aerobic bacteria lost the biochemical pathway required forplasmalogen synthesis. Humans are able to synthesize plasmalogens(although via a different biochemical pathway from anaerobes), althoughthese were generally found to have longer chain lengths than bacterialplasmalogens.

Other biomarkers that are indicative of a certain group of bacteriainclude, for instance, lipopeptides that are produced specifically bycertain Bacillus species, such as, surfactin for B. subtilis andlichenysin for B. licheniformis. Production of these two molecules alsoenables straightforward differentiation of these otherwise very closelyrelated bacteria. A further example includes PQS-derived quorum-sensingmolecules and mono- and di-rhamnolipid species found for Pseudomonasaeruginosa.

Results are shown in Tables 1 et seq.

Analysis of Mycoplasma-Infected Cell Lines

Cell cultures frequently get infected by Mycoplasma, a genus of bacteriathat lack a cell wall around their cell membrane. Mycoplasma infectioncan alter many physiological processes and thus lead to misleadingexperimental results if a study is performed using infected cells.Plasmocin® (InvivoGen, San Diego, Calif., USA) is a commerciallyavailable antibiotic treatment that is frequently used to eradicatemycoplasma infection in cell cultures. The method provided herein usingREIMS technology was used to generate spectrometric data fromMycoplasma-free, Mycoplasma-infected and plasmocin® cured HeLa and HEKcell lines.

ANOVA tests were performed to determine significant differences betweenMycoplasma positive and negative samples. Adjusted p-values wereobtained using the adaptive Benjamini-Hochberg (BH) procedure to correctfor multiple testing. FIG. 36 shows the time-dependent raw intensitiesin course of plasmocin® treatment of the mycoplasma infection in case ofm/z=747.5183.

Sampling point #1 corresponds with day 1 and the original mycoplasmapositive or negative sample and sampling point #2 corresponds with day 2and the addition of plasmocin® antibiotic. Sampling point #3 correspondswith day 3. Sampling point #4 corresponds with the removal of plasmocin®antibiotic. Sampling point #5 corresponds with all samples beingplasmocin® free.

As shown in FIG. 37A, 23 and 386 binned m/z signals were significantlyhigher in Mycoplasma-infected HEK and HeLa cells, respectively. Thehigher number of significantly increased peaks may be explained with ahigher number of sampling points (contributing to higher power insignificance testing) or may reflect the increased reactivity of HeLacells to Mycoplasma infection. Interestingly, we found no signalsshowing reduced intensity in Mycoplasma-infected cell lines (p=0.15).Table 13 lists the annotation of the 18 m/z signals that were found tobe significantly increased across all Mycoplasma-infected cells(p=1.37E-20). As an example, changes in the intensity of m/z 819.52(identified as PG(40:7) based on exact mass measurements) are shown inMycoplasma-free, Mycoplasma-infected and Plasmocin™-treated HEK and HeLacells (FIGS. 38A and B). This m/z value, along with the signalcorresponding to its isotope, was found to be increased inMycoplasma-infected HeLA and HEK cells, whereas the intensity returnedto pre-infection levels upon successful Plasmocin™ treatment. Similarresults were obtained for the other m/z signals shown in Table 13.

In the 18 dimensional space of these m/z signals, Mycoplasma-infectedand Mycoplasma-free HEK and HeLa samples were analyzed by PCA (FIG.37B). The first principal component (PC1) reveals differences betweenthe two different cell lines while the second PC separatesMycoplasma-free and Mycoplasma-infected samples.

Trends in the spectral intensities of these species were found to besignificantly different in case of the healthy and infected cell lines,suggesting that the lipid metabolism has been perturbed by mycoplasmainfection. The above approach demonstrates the applicability of themethod provided herein to study changes during Mycoplasma infection andas possible use for Mycoplasma screening.

Detection of Bacteria in Human Colorectal Tissue Specimens

The inventors attempted to visualise the presence and distribution ofbacteria in human colorectal tissue specimens. Bacteria are known tocover the mucosal membranes in the gut and the gut microbial communityis arguably most extensively studied and characterised. The analysis wasperformed by generating single ion images for the taxonomical markersthat are listed in Table 14. Bacteria could be visualised in >90% ofanalysed colorectal specimens, including healthy and cancerous tissuespecimens. Among cancerous specimens, bacteria were largely foundlocalised in areas that were identified as necrotic by histopathologicalexamination of the H&E stained tissue sections. However, bacteria werealso frequently detected along healthy mucosa.

Analysis of Necrotic Tissue

FIG. 39 shows the tissue type-distribution of a cancerous tissuespecimen that originated from the centre of tumour dissected during aright hemicolectomy. Histopathological examination revealed the presenceof cancerous and stromal tissue.

Mass spectra of the necrotic tissue area as well as surroundingcancerous and stromal tissue are shown in FIG. 39 and display a markedlydifferent phospholipid composition for the necrotic area compared toviable human tissue, namely a significantly reduced glycerophospholipidcontent and a variety of lower molecular weight sphingolipid-derivedtaxonomic marker species in the mass range of m/z=500-700.

When visualising these taxonomical markers, the respective single ionimages were found to largely display co-localisation of the taxonomicalmarker molecules and thus bacterial cells. An array of co-localisedsingle ion images of homologous molecules are displayed in FIG. 40 andcould be attributed to the Bacteroidetes phylum. Iso-C15:0-substitutedphosphoglycerol dihydroceramides were found to be specific for thePorphyromonadaceae family (part of Bacteroidetes phylum), which in thisstudy were only represented by Parabacteroides spp., however, namedcompounds were reported present in high abundance in Porphyromonasgingivalis, suggesting general applicability of this marker for thisfamily. Members of the Bacteroidetes phylum were reported in metagenomicstudies to be accountable for up to 50% of the gut microbial community.However, taxon-specific markers for Bacteroidetes fragilis were notdetected suggesting that the Bacteroidetes bacteria present do notcontain a high amount of the opportunistic pathogen B. fragilis.

FIG. 41 shows single ion images of further taxonomical markers whichwere found to be specific for the Bacteroidetes phylum, among thosedihydroceramide and a related compound with two more double-bonds (orequivalents). The compound at m/z=639.4954 was found to be a homologueof the lipid species at m/z=653.5113 mentioned earlier. A signal atm/z=566.4790 indicates the presence of members of the Flavobacteriaclass. Specific plasmalogen species for Clostridiales and Fusobacteriawere additionally found, as well as an odd numbered PE that showsspecificity for the Enterobacteriales order. All of these bacterialclasses are capable of living under anaerobic conditions and werereported to be major components of the human gut microbiome.

While members of the Bacteroidetes phylum largely cluster around theleft hand side of the tissue section where necrotic areas wereidentified, Clostridiales and Fusobacteria were additionally detected inat a spot more centred within the tissue section, thus confirming theexpectation that not all bacterial species show identical localisation.The large bacterial presence observed in the necrotic tissue areas istentatively associated with the lack of immunoresponse of the humanbody, which enables bacteria to multiply largely uncontrolled.

Detection of Bacteria in Healthy Mucosa

FIG. 39 shows the tissue type-distribution of a healthy tissue specimenthat originated from a right hemicolectomy. It originated from healthycolon tissue 5 cm distance from the centre of tumour. Histopathologicalexamination revealed healthy mucosa and submucosa, divided by themuscularis mucosae layer. Additionally, two lymphoid aggregates(inflammation) can be observed.

FIG. 40 shows single ion images for those taxon-specific markers thatwere detected in this sample. Generally, far fewer and less intensesignals were observed than for necrotic tissue. This is tentativelyattributed to the healthy immune response that restricts unlimitedbacterial growth as was observed in the necrotic tissue specimen.However, the two main bacterial components of the commensal humanmicrobiome could still be detected, namely members of the Bacteroidetesphylum and Clostridiaceae family.

Metagenomic characterisations were performed for this sample andconfirmed the presence of large amounts of Bacteroidetes, Proteobacteriaand Firmicutes which on class level were largely attributable toClostridia, Bacteroidia, and Gamma-Proteobacteria, respectively.

This study demonstrates that molecular species differ significantlybetween microbial lipidomes and the human tissue lipidome.Taxon-specific markers for a variety of bacterial types were shown to beabsent in human lipidomes/metabolome and can thus be used to visualizethe presence of bacteria in human samples, as shown for human colorectaltissues. It was further demonstrated that taxonomic markers derived bythe REIMS technique can be used in conjunction with other massspectrometric ionization techniques detecting lipid profiles, such as,DESI.

Detection of Bacteria Using DESI

FIG. 43A shows desorption electrospray ionisation (“DESI”) massspectrometry analysis of a bacteria (Klebsiella pneumonia) sample on aswab in accordance with an embodiment. The data illustrated in FIG. 43Ashows that bacterial samples can be detected using desorptionelectrospray ionisation (“DESI”) mass spectrometry on swabs, accordingto various embodiments. FIG. 43B shows for comparison rapid evaporativeionisation mass spectrometry (“REIMS”) time of flight (“TOF”) massspectrometry data of a corresponding bacterial sample measured directlyfrom an agar plate. The peaks highlighted by stars were detected withboth ionisation techniques.

Desorption electrospray ionisation (“DESI”) swab analysis formicroorganism detection was further tested on six cultivated speciesincluding Candida albicans, Pseudomonas montelli, Staphylococcusepidermis, Moraxella catarrhalis, Klebsiella pneumonia and Lactobacillussp. These are all important bacteria and fungi species that wereisolated from vaginal mucosal membranes of pregnant patients and whichwere identified by sequence analysis such as 16S rRNA gene sequencing.

A swab was quickly dipped into a solution of diluted biomass from eachspecies in 10 μL methanol, followed by desorption electrosprayionisation (“DESI”) mass spectrometry analysis of the swab surface.

FIGS. 44A-C show microorganism analysis using desorption electrosprayionisation (“DESI”) mass spectrometry on swabs.

FIG. 44A shows averaged desorption electrospray ionisation (“DESI”) massspectra of diverse analysed microorganism species including Candidaalbicans, Pseudomonas montelli, Staphylococcus epidermis, Moraxellacatarrhalis, Klebsiella pneumonia and Lactobacillus sp.

FIGS. 44B and 44C show PCA plots showing a separation between thevaginal mucosa (pregnant and non-pregnant group) and the microorganismspecies within the first two components. In addition, a separation canbe observed between the different bacteria and fungi species.

Unique spectral features were observed in the mass spectra as shown inFIG. 44A resulting in the ability to separate between differentmicroorganism classes as well as from the vaginal mucosa in the PCAscore plots (FIGS. 44B and 44C) within the first two components.

This result shows the potential to characterise microbe, e.g.,bacteria-specific and host-response metabolite biomarkers and signaturesfrom specific microbial, e.g., bacterial communities from the animal,e.g., human mucosal membrane using desorption electrospray ionisation(“DESI”) mass spectrometry on medical swabs.

Example of Data Analysis

Raw mass spectrometric files were converted into mzML format andsubsequently imported as imzML format (REF) into MATLAB (Mathworks,Natick, Mass.; http://www.mathworks.co.uk/) for data pre-processing.REIMS spectra were linearly interpolated to a common sampling intervalof 0.01 Da. Recursive segment wise peak alignment was then used toremove small mass shifts in peak positions across spectral profiles. Thealigned data were subjected to total ion count (TIC) data normalizationand log-based transformation. Pattern recognition analysis andvisualization were performed either in Matlab or in RStudio (Boston,Mass., USA, see also www.r-project.com). Only the mass range of m/z150-1000 was used for data analysis. For self-identity experiments, thedata set was filtered to keep a reduced set of m/z values: a m/z valuewas kept, if the difference between the available samples weresignificantly different at alpha=0.01 threshold level based on theKruskal-Wallis test.

Ionic species in the mass spectra were identified based on exact massmeasurements (mass deviation <3 ppm) and MS/MS fragmentation patterns.

Faecal Analysis Using REIMS

-   1. Take a sample, e.g., a 10 μl loop of fresh or, if frozen, a    defrosted sample of stool.-   2. If using forceps based REIMS, take a small amount between the    forceps and draw the probes together.-   3. Perform REIMS analysis, e.g., using previously described    parameters for REIMS.

REIMS Analysis of Blood Culture Pellets

Objective: This protocol describes a specific example of a procedure foranalysing blood culture samples using REIMS analysis.

Initially, inoculate 10 ml of defibrinated horse blood with a singlemicrobial colony. Grow this aerobically at 37° C. for 24 hours. Next,inoculate 1 l of horse blood with 1 ml of the overnight culture. Growaerobically at 37° C. and at time 0 and each hour thereafter remove 25ml to analyse in the following way:

a. Transfer 10 ml into a 50 falcon tube and centrifuge the sample for 10mins at 3,2000 g. Use REIMS to analyse the pellet as described below.

b. Make a 2.5% Microbiology grade agar solution using HPLC water andheat until the solution reaches 50° C. Leave to stand for 1 minute toremove air bubbles. Next, add 2 ml of this to 8 ml of the blood culturedescribed above and mix gently by pipetting. Pour into a small agarplate and allow to set for 15 minutes. Use this to perform REIMSanalysis.

c. With 1 ml of this solution make serial dilutions to 10-6 usingmolecular grade water, and plate 100 μl of each onto a blood agar plate.Incubate for 24 hours and after count the number of colonies todetermine the CFU.

d. Use a further 2 ml of the blood culture and freeze at −80° C. forLC-MS analysis. REIMS analysis may be performed on the centrifugedpellet and/or the agarose block.

Although the present invention has been described with reference topreferred embodiments, it will be understood by those skilled in the artthat various changes in form and detail may be made without departingfrom the scope of the invention as set forth in the accompanying claims.

TABLE 1 Table of biomarkers: phospholipids and their mass spectralsignals Identified phospholipids detected in the mass range m/z =600-900 for several analysed microbial species. Only phospholipids withrelative abundances >5% and only the most abundant acyl chaincombination were included. Solid growth media on which bacteria weregrown is given in parentheses. ID based solely on exact mass when lipidcomposition given as sum carbon number rather than individual acylchains. Nominal mass m/z C. koseri (CBA) E. coli (CBA) K. pneumoniae(LB) P. mirabilis (MCC) P. aeruginosa (LB) S. marascens (MCC) S. aureus(CBA) S. agaiactiae (CBA) S. pyogenes (CBA) 645 PA(32:1)* 659PA(16:0/17:1) PA(16:0/17:1) PA(16:0/17:1) 661 PA(33:0)* 665PG(12:0/16:0) 671 PA(34:2)* 673 PA(16:0/18:1) PA(16:0/18:1)PA(16:0/18:1)* 675 PG(15:0/15:0-H₂O) PG(30:0-H₂O)* 688 PE(16:1/16:0)PE(16:1/16:0) 691 PG(14:0/16:1) 693 PG(16:0/14:0) PG(16:0/14:0)PG(15:0/15:0) PG(15:0/15:0) PG(14:0/16:0) 697 PA(36:3)* 699PA(18:1/18:1)* 701 PG(32:1)-H₂O* PG(32:1)-H₂O* 702 PE(16:0/17:1)PE(16:0/17:1) PE(16:0/17:1) PE(16:0/17:1) PE(16:0/17:1) 707PG(15:0/16:0) 716 PE(18:1/16:0) PE(18:1/16:0) PE(18:1/16:0)PE(17:0/17:1) 717 PG(32:2)* PG(16:1/16:1) 719 PG(16:1/16:0)PG(16:1/16:0) PG(16:0/16:1) PG(16:0/16:1) PG(16:0/16:1) PG(16:0/16:1)PG(16:0/16:1) PG(16:0/16:1) 721 PG(15:0/17:0) PG(15:0/17:0)PG(16:0/16:0) 725 PA(16:1/18:2) 727 PG(16:1/18:1)-H₂O 729PG(16:0/18:1)-H₂O* PG(16:0/18:1)-H₂O 730 PE(16:0/19:1) 733 PG(16:0/17:1)PG(16:0/17:1) PG(16:0/17:1) PG(16:0/17:1) PG(16:0/17:1) PG(16:0/17:1)735 PG(15:0/18:0) 743 PG(16:0/18:3) PG(16:1/18:2) 745 PG(16:1/18:1)PG(16:1/18:1) PG(16:1/18:1) PG(16:1/18:1) PG(16:1/18:1) PG(16:0/18:2)*PG(16:1/18:1) 747 PG(16:0/18:1) PG(16:0/18:1) PG(16:0/18:1)PG(16:0/18:1) PG(16:0/18:1) PG(16:0/18:1) PG(16:0/18:1) PG(16:0/18:1)749 PG(15:0/19:0) PG(15:0/19:0) PG(16:0/18:1)* 752 759 PG(17:1/18:1)PG(17:1/18:1) PG(17:1/18:1) PG(17:1/18:1) 761 PG(16:0/19:1)PG(16:0/19:1) PG(16:0/19:1) PG(16:0/19:1) PG(16:0/19:1) 763PG(15:0/20:0) 770 PE(38:2)* 771 PG(36:3)* PG(18:1/18:1)* 773PG(18:1/18:1) PG(18:1/18:1) PG(17:1/19:1) PG(17:1/19:1) PG(18:1/18:1)PG(36:2)* PG(18:1/18:1) 775 PG(36:1)* PG(18:0/18:1) 787 PG(18:1/19:1)801 PG(19:1/19:1) *Signal intensity not sufficient to obtain meaningfulMS/MS data; Abbreviations: PG = phosphatidylglycerol, PE =phosphatidylethanolamine, CBA = Columbia blood agar, LB = lysogenicbroth agar, MCC = McConkey agar.

TABLE 2 Table of biomarkers: cardiolipins and their mass spectralsignals Cardiolipin species that were identified for Staphylococcusepidermidis ATCC 12228. Sum Exact mass Exp. Mass Compound formula [M −H]− mass Deviation CL(62:0) C₇₁H₁₃₈O₁₇P₂ 1323.9335 1323.9268 5.0 ppmCL(63:0) C₇₂H₁₄₀O₁₇P₂ 1337.9492 1337.9426 4.9 ppm CL(64:0) C₇₃H₁₄₂O₁₇P₂1351.9649 1351.9601 3.6 ppm CL(65:0) C₇₄H₁₄₄O₁₇P₂ 1365.9806 1365.97583.5 ppm CL(66:0) C₇₅H₁₄₆O₁₇P₂ 1379.9962 1379.9913 3.5 ppm CL(67:0)C₇₆H₁₄₈O₁₇P₂ 1394.0119 1394.0070 3.5 ppm CL(68:0) C₇₇H₁₅₀O₁₇P₂ 1408.02751408.0238 2.6 ppm CL(69:0) C₇₈H₁₅₂O₁₇P₂ 1422.0432 1422.0400 2.3 ppmCL(70:0) C₇₉H₁₅₄O₁₇P₂ 1436.0588 1436.0561 1.9 ppm CL(71:0) C₈₀H₁₅₆O₁₇P₂1450.0745 1450.0748 0.2 ppm CL(72:0) C₈₁H₁₅₈O₁₇P₂ 1464.0900 1464.09704.8 ppm

TABLE 3 Table of biomarkers: mycolic acids and their mass spectralsignals Identified mycolic acids as detected in differentCorynebacterium species. Sum Exact mass Exp. Mass MS/MS Compound formula[M − H]− mass Deviation fragments alpha-Mycolic acid C28:0 C₂₈H₅₅O₃439.415669 439.4159 0.5 ppm — alpha-Mycolic acid C30:0 C₃₀H₅₉O₃467.446969 467.4473 0.7 ppm 227 (C14:0), 255 (C16:0) alpha-Mycolic acidC32:1 C₃₂H₆₁O₃ 493.462619 493.4634 1.6 ppm — alpha-Mycolic acid C32:0C₃₂H₆₃O₃ 495.478269 495.4786 0.7 ppm 255 (C16:0) alpha-Mycolic acidC34:2 C₃₄H₆₃O₃ 519.478269 519.4788 1.0 ppm — alpha-Mycolic acid C34:1C₃₄H₆₅O₃ 521.493919 521.4942 0.5 ppm 255 (C16:0), 281 (C18:1)alpha-Mycolic acid C36:2 C₃₆H₆₇O₃ 547.509569 547.5102 1.2 ppm 281(C18:1)

TABLE 4 Table of biomarkers: mycolic acids and their mass spectralsignals Identified mycolic acids as detected in Rhodococcus species. SumExact mass Exp. Mass Compound formula [M − H]− mass Deviationalpha-Mycolic acid C28:0 C₂₈H₅₆O₃ 439.4157 439.4159 0.5 ppmalpha-Mycolic acid C30:1 C₃₀H₅₈O₃ 465.4313 465.4315 0.4 ppmalpha-Mycolic acid C30:0 C₃₀H₆₀O₃ 467.4470 467.4472 0.4 ppmalpha-Mycolic acid C31:1 C₃₁H₆₀O₃ 479.4470 479.4473 0.6 ppmalpha-Mycolic acid C31:0 C₃₁H₆₂O₃ 481.4626 481.4630 0.8 ppmalpha-Mycolic acid C32:2 C₃₂H₆₀O₃ 491.4470 491.4475 1.0 ppmalpha-Mycolic acid C32:1 C₃₂H₆₂O₃ 493.4626 493.4634 1.6 ppmalpha-Mycolic acid C32:0 C₃₂H₆₄O₃ 495.4783 495.4786 0.6 ppmalpha-Mycolic acid C33:2 C₃₃H₆₂O₃ 505.4626 505.4630 0.8 ppmalpha-Mycolic acid C33:1 C₃₃H₆₄O₃ 507.4783 507.4785 0.4 ppmalpha-Mycolic acid C33:0 C₃₃H₆₆O₃ 509.4939 509.4943 0.8 ppmalpha-Mycolic acid C34:3 C₃₄H₆₂O₃ 517.4626 517.4632 1.2 ppmalpha-Mycolic acid C34:2 C₃₄H₆₄O₃ 519.4783 519.4788 1.0 ppmalpha-Mycolic acid C34:1 C₃₄H₆₆O₃ 521.4939 521.4944 1.0 ppmalpha-Mycolic acid C34:0 C₃₄H₆₈O₃ 523.5096 523.5100 0.8 ppmalpha-Mycolic acid C35:3 C₃₅H₆₄O₃ 531.4783 531.4784 0.2 ppmalpha-Mycolic acid C35:2 C₃₅H₆₆O₃ 533.4939 533.4946 1.3 ppmalpha-Mycolic acid C35:1 C₃₅H₆₈O₃ 535.5096 535.5100 0.7 ppmalpha-Mycolic acid C35:0 C₃₅H₇₀O₃ 537.5252 537.5259 1.3 ppmalpha-Mycolic acid C36:3 C₃₆H₆₆O₃ 545.4939 545.4944 0.9 ppmalpha-Mycolic acid C36:2 C₃₆H₆₈O₃ 547.5096 547.5102 1.1 ppmalpha-Mycolic acid C36:1 C₃₆H₇₀O₃ 549.5252 549.5260 1.5 ppmalpha-Mycolic acid C36:0 C₃₆H₇₂O₃ 551.5409 551.5424 2.7 ppmalpha-Mycolic acid C37:3 C₃₇H₆₈O₃ 559.5096 559.5102 1.1 ppmalpha-Mycolic acid C37:2 C₃₇H₇₀O₃ 561.5252 561.5257 0.9 ppmalpha-Mycolic acid C37:1 C₃₇H₇₂O₃ 563.5409 563.5418 1.6 ppmalpha-Mycolic acid C37:0 C₃₇H₇₄O₃ 565.5565 565.5573 1.4 ppmalpha-Mycolic acid C38:4 C₃₈H₇₄O₃ 571.5096 571.5098 0.3 ppmalpha-Mycolic acid C38:3 C₃₈H₇₄O₃ 573.5252 573.5261 1.6 ppmalpha-Mycolic acid C38:2 C₃₈H₇₄O₃ 575.5409 575.5415 1.0 ppmalpha-Mycolic acid C38:1 C₃₈H₇₄O₃ 577.5565 577.5579 2.4 ppmalpha-Mycolic acid C39:2 C₃₈H₇₆O₃ 589.5565 589.5578 2.2 ppm

TABLE 5 Table of biomarkers: mycolic acids and their mass spectralsignals Identified mycolic acids as detected in Nocardia species. SumExact mass Exp. Mass Compound formula [M − H]− mass Deviationalpha-Mycolic acid C48:3 C₄₈H₉₀O₃ 713.6817 713.6797 2.8 ppmalpha-Mycolic acid C48:2 C₄₈H₉₂O₃ 715.6974 715.6959 2.1 ppmalpha-Mycolic acid C50:3 C₅₀H₉₄O₃ 741.7130 741.7114 2.2 ppmalpha-Mycolic acid C50:2 C₅₀H₉₆O₃ 743.7287 743.7285 0.3 ppmalpha-Mycolic acid C52:3 C₅₂H₉₄O₃ 769.7443 769.7430 1.7 ppmalpha-Mycolic acid C52:2 C₅₂H₉₆O₃ 771.7600 771.7588 1.6 ppmalpha-Mycolic acid C53:3 C₅₃H₉₆O₃ 783.7600 783.7596 0.5 ppmalpha-Mycolic acid C53:2 C₅₃H₉₄O₃ 785.7756 785.7754 0.3 ppmalpha-Mycolic acid C54:4 C₅₄H₉₆O₃ 795.7600 795.7594 0.8 ppmalpha-Mycolic acid C54:3 C₅₄H₉₈O₃ 797.7756 797.7739 2.1 ppmalpha-Mycolic acid C54:2 C₅₄H₁₀₀O₃ 799.7913 799.7902 1.4 ppmalpha-Mycolic acid C55:4 C₅₄H₁₀₂O₃ 809.7756 809.7748 1.0 ppmalpha-Mycolic acid C55:3 C₅₄H₁₀₄O₃ 811.7913 811.7907 0.7 ppmalpha-Mycolic acid C55:2 C₅₄H₁₀₆O₃ 813.8069 813.8061 1.0 ppmalpha-Mycolic acid C56:5 C₅₆H₁₀₂O₃ 821.7756 821.7748 1.0 ppmalpha-Mycolic acid C56:4 C₅₆H₁₀₄O₃ 823.7913 823.7907 0.7 ppmalpha-Mycolic acid C56:3 C₅₆H₁₀₆O₃ 825.8069 825.8053 1.9 ppmalpha-Mycolic acid C56:2 C₅₆H₁₀₈O₃ 827.8226 827.8213 1.6 ppmalpha-Mycolic acid C57:4 C₅₇H₁₀₆O₃ 837.8069 837.8050 2.3 ppmalpha-Mycolic acid C57:3 C₅₇H₁₀₈O₃ 839.8226 839.8215 1.3 ppmalpha-Mycolic acid C58:5 C₅₈H₁₀₆O₃ 849.8069 849.8068 0.1 ppmalpha-Mycolic acid C58:4 C₅₈H₁₀₈O₃ 851.8226 851.8218 0.9 ppmalpha-Mycolic acid C58:3 C₅₈H₁₁₀O₃ 853.8382 853.8375 0.8 ppmalpha-Mycolic acid C59:3 C₅₉H₁₁₂O₃ 867.8539 867.8537 0.2 ppmalpha-Mycolic acid C60:4 C₆₀H₁₁₂O₃ 879.8539 879.8537 0.2 ppmalpha-Mycolic acid C60:3 C₆₀H₁₁₄O₃ 881.8695 881.8683 1.4 ppm

TABLE 6 Table of biomarkers: mycolic acids and their mass spectralsignals Identified mycolic acids as detected in different Mycobacteriumspecies. Sum Exact mass Exp. Mass Compound formula [M − H]− massDeviation alpha-Mycolic acid C77:2 C₇₇H₁₅₀O₃ 1122.1512 1122.1525 1.2 ppmalpha-Mycolic acid C78:2 C₇₈H₁₅₂O₃ 1136.1669 1136.1684 1.3 ppmalpha-Mycolic acid C79:2 C₇₉H₁₅₄O₃ 1150.1825 1150.1833 0.7 ppmEpoxy/keto-Mycolic acid C79:1 or C₇₉H₁₅₄O₄ 1166.1774 1166.1769 0.4 ppmMethoxy-Mycolic acid C79:2 Epoxy/keto-Mycolic acid C80:1 or C₈₀H₁₅₆O₄1180.1931 1180.1897 2.9 ppm Methoxy-Mycolic acid C80:2Epoxy/keto-Mycolic acid C81:1 or C₈₁H₁₅₈O₃ 1194.2087 1194.2102 1.3 ppmMethoxy-Mycolic acid C81:2

TABLE 7 Table of biomarkers: sphingolipids and their mass spectralsignals. Identified sphingolipid species in members of the Bacteroidetesphylum Experimental Mass Formula mass Exact mass Deviation Observed inCeramide Phosphorylethanolamine/Phosphoethanolamine Dihydroceramides(PE-DHC) C₃₆H₇₄N₂O₇P⁻ 677.5253 677.5239 2.0 B. fragilis, B. ovatus, B.thetaiotaomicron, C₃₇H₇₆N₂O₇P⁻ 691.5411 691.5396 2.2 B. uniformis, B.vulgatus, P. bivia, C₃₈H₇₈N₂O₇P⁻ 705.5569 705.5552 2.4 P. distonasisCeramides C₃₄H₆₉NO₄Cl⁻ 590.4934^(a) 590.4921 2.2 B. fragilis, B. ovatus,B. thetaiotaomicron, C₃₅H₇₁NO₄Cl⁻ 604.5090 604.5077 2.1 B. uniformis, B.vulgatus, P. bivia, C₃₆H₇₃NO₄Cl⁻ 618.5246 618.5234 1.9 P. distonasisBacteroides fragilis α-Galactosylceramides C₄₀H₇₉NO₉Cl⁻ 752.5465752.5449 2.1 B. fragilis C₄₁H₈₁NO₉Cl⁻ 766.5623 766.5605 2.3 C₄₂H₈₃NO₉Cl⁻780.5781 780.5762 2.4 C15:0 substituted Phosphoglycerol Dihydroceramides(subPG-DHC) C₅₀H₁₀₀O₁₀NP 904.7007 904.7028 2.3 B. fragilis, B. ovatus,B. thetaiotaomicron, C₅₁H₁₀₂O₁₀NP 918.7163 918.7185 2.4 B. uniformis, B.vulgatus, P. distonasis C₅₂H₁₀₄O₁₀NP 932.7324^(b) 932.7337 1.4C₅₃H₁₀₆O₁₀NP 946.7481^(b) 946.7484 0.3 C₅₄H₁₀₈O₁₀NP 960.7637^(b)960.7624 1.3 Unsubstituted Phosphoglycerol Dihydroceramides (unPG-DHC)C₃₇H₇₆O₉NP 708.5184 708.5199 2.1 P. distonasis C₃₉H₈₀O₉NP 736.5497736.5484 1.8

TABLE 8 Table of biomarkers: quorum-sensing molecules and their massspectral signals Identified quorum-sensing molecules in Psuedomonasaeruginosa. Sum Exp. Mass Compound formula Exact mass mass Deviation2-Heptylquinoline-4(1H)-one C₁₆H₂₁NO [M − H]⁻ = 242.1550 242.1552 −0.8ppm 2-Heptyl-3-hydroxy-4(1H)- C₁₆H₂₁NO₂ [M − H]⁻ = 258.1499 258.1502−1.2 ppm quinolone (PQS) Hydroxynonenylquinoline C₁₈H₂₃NO [M − H]⁻ =268.1707 268.1711 −1.5 ppm Hydroxynonylquinoline C₁₈H₂₅NO [M − H]⁻ =270.1863 270.1868 −1.9 ppm Hydroxyundecenylquinoline C₂₀H₂₆NO [M − H]⁻ =296.2020 296.2023 −1.0 ppm

TABLE 9 Table of biomarkers: Rhamnolipids and their mass spectralsignals. Rhamnolipid species commonly produced by P. aeruginosa strains.Sum Exact mass Exp. Mass Compound formula [M − H]− mass DeviationRha-C₂₀ C₂₆H₄₈O₉ 503.3225 503.3224 0.2 ppm Rha-C_(22:1) C₂₈H₅₀O₉529.3382 529.3384 −0.4 ppm   Rha-C₂₂ C₂₈H₅₂O₉ 531.3539 531.3538 0.2 ppmRha-Rha-C₂₀ C₃₂H₅₈O₁₃ 649.3805 649.3804 0.2 ppm Rha-Rha-C₂₂ C₃₄H₆₂O₁₃677.4118 677.4116 −0.3 ppm   Rha-Rha-C_(22:1) C₃₄H₆₀O₁₃ 675.3961675.3965 −0.6 ppm  

TABLE 10 Table of biomarkers: Surfactins and their mass spectralsignals. Surfactin species detected in positive and negative ion modefor Bacillus subtilis. Negative ion mode Positive ion mode Exact ExactExp. mass Exp. mass Compound Mass [M − H]⁻ Δppm mass [M + Na]⁺ ΔppmSurfactin 1006.6453 1006.6440 1.3 1030.6389 1030.6416 2.6 (C13)Surfactin 1020.6604 1020.6597 0.7 1044.6545 1044.6573 2.7 (C14)Surfactin 1034.6754 1034.6753 0.1 1058.6702 1058.6729 2.6 (C15)

TABLE 11 Table of biomarkers: Lichenysins and their mass spectralsignals Lichenysin compounds detected in Bacillus licheniformis. Exp.Exact mass Compound mass [M − H]− Δppm Lichenysin (C13) 1005.65941005.6600 0.6 Lichenysin (C14) 1019.6748 1019.6756 0.8 Lichenysin (C15)1033.6906 1033.6913 0.7 Lichenysin (C16) 1047.7055 1047.7070 1.4

TABLE 12 Table of biomarkers Mass spectrometric signals that show strongpositive correlation with the ugcg gene expression for a cell line(NCI60) dataset. Corre- lation Exp. Exact coef- mass mass Δppm TentativeID Formula Adduct ficient 734.5355 734.5343 0.2 GlyCer C₄₀H₇₇NO₈ [M +0.552 (d18:1/16:0) Cl]⁻ 818.6295 818.6282 0.2 GlyCer C₄₆H₈₉NO₈ [M +0.662 (d18:1/22:0) Cl]⁻ 842.6312 842.6332 −0.2 GlyCer C₄₈H₈₉NO₈ [M +0.602 (d18:1/24:2) Cl]⁻ 844.6451 844.6439 0.1 GlyCer C₄₈H₉₁NO₈ [M +0.668 (d18:1/24:1) Cl]⁻ 846.6627 846.6595 0.4 GlyCer C₄₈H₉₃NO₈ [M +0.688 (d18:1/24:0) Cl]⁻ 872.6733 872.6752 −0.2 GlyCer C₅₀H₉₅NO₈ [M +0.707 (d18:1/26:1) Cl]⁻

TABLE 13 Table of biomarkers for Mycoplasma List of m/z peak that aresignificantly higher in Mycoplasma infected samples compared toMycoplasma free samples in both HEK and HeLa cell lines. Column 2displays the corresponding binned peak, column 2 highlights putativeisotope peaks, while column 4 shows the tentative annotation of thebinned peak. Phosphatidylglycerol and sphingomyelin species, that aremain Mycoplasma constituents are written in bold. significantly dif-corresponding ferent binned m/z m/z signal Annotation 687.54 687.5468722.51 722.5156 PE(P-36:4) 733.53 733.5231 PE(P-38:4) 747.52 747.5193PG(34:1) 748.53 748.5243 Isotope of m/z = 747.52 753.51 753.5090PG(P-36:4) 764.52 764.5264 PE(38:5) 764.53 764.5262 PE(38:5) 766.53766.5412 PE(38:4) 773.54 773.5359 PG(36:2) 774.54 774.5391 PG(36:2),Isotope of m/z = 773.54 774.55 774.5391 PG(36:2), Isotope of m/z =773.54 775.56 775.5520 PG(36:1) 776.56 776.5564 PG(36:1), Isotope of m/z= 775.56 776.57 776.5564 PG(36:1), Isotope of m/z = 775.56 819.52819.5189 PG(40:7) 820.53 820.5268 PG(40:7), Isotope of m/z = 819.52820.54 820.5268 PG(40:7), Isotope of m/z = 819.52

TABLE 14 Taxon-specific biomarkers. No markers were calculated where thesize of sample set was insufficient. Phylum Class Order Family GenusSpecies No. Gram- Bacter- Bacteroidetes Bacteroidales BacteroidaceaeBacteroides Bacteroides acidifaciens 2 neg- oidetes 616.5094 576.4764Bacteroides caccae 2 ative 381.2765 617.5124 820.7522 Bacteroideseggerthii 2 393.2764 618.5233 Bacteroides fragilis 5 590.4923 619.5273Bacteroides helcogenes 1 591.4963 620.5184 Bacteroides ovatus 3 592.4883627.4883 Bacteroides pyogenes 1 604.5083 628.4913 Bacteroides 3 605.5113635.5004 thetaiotaomicron 3 606.5033 636.5044 Bacteroides uniformis 3616.4724 637.5044 Bacteroides vulgatus 623.5024 644.5033 Porphyro- Para-Parabacteroides 5 624.5054 648.5003 monadaceae bacteroides distasonis 2637.5044 697.5743 814.7063 Parabacteroides 639.4954 698.5763 815.7112johnsonii 640.4993 711.5902 828.7232 653.5113 712.5933 829.7262 654.5143840.6842 677.5238 841.6942 691.5395 843.7432 705.5562 854.7022 858.6972872.7072 908.7401 909.7431 910.7471 918.7191 921.7912 932.7332 933.7362934.7422 944.7342 945.7372 946.7472 947.7502 948.7562 949.7592 958.7461959.7501 960.7611 961.7661 962.7691 Prevotellaceae Prevotella Prevotellabivia 7 661.5283 675.5453 676.5503 870.8002 908.7401 922.7552 923.7612953.5113 Rikenellaceae Alistipes Alistipes onderdonkii 1 FlavobacteriaFlavo- Flavo- Chryseo- Chryseobacterium 3 324.2545 bacterialesbacteriaceae bacterium indologenes 1 333.2084 Chryseobacterium sp390.2324 Elizabeth- Elizabethkingia 4 392.2484 kingia meningoseptica393.2504 Myroides Myroides odoratimimus 2 552.4643 553.4674 553.4674554.4714 556.4034 565.4654 566.4794 567.4834 568.4864 600.4664 601.4723618.4773 619.4813 620.4883 651.4953 651.4953 891.7411 Fuso- FusobacteriaFuso- Fuso- Fuso- Fusobacterium 3 bacteria bacteriales bacteriaceaebacterium gonidiaformans 7 227.2015 Fusobacterium 4 644.4652 necrophorum1 645.4633 Fusobacterium 646.4833 peridontiam 647.4812 Fusobacterium sp648.4832 673.4443 696.4953 714.5492 856.6782 865.6632 884.7083 Proteo-Alpha- Caulo- Caulo- Brevundi- Brevundimonas 2 bacteria Proteo-bacterales bacteraceae monas diminuta 768.5182 bacteria 769.5502782.5342 770.5562 783.5293 771.5582 795.5572 797.5723 818.5673 957.6261Rhizobiales Rhizobiaceae Rhizobium Rhizobium radiobacter 5 439.4155440.4195 739.5313 784.5902 785.5932 799.5132 Rhodo- Aceto- RoseomonasRoseomonas mucosa 6 spirillales bacteraceae Roseomonas sp 1 662.5393722.5753 729.5813 733.5752 733.6173 734.5753 747.6283 757.6173 Beta-Burk- Alcaligenaceae Achromo- Achromobacter sp 3 Proteobacteriaholderiales bacter Achromobacter 3 xylosoxidans Alcaligenes Alcaligenesfaecalis 3 Burkholder- Burkholderia Burkholderia cepacia 7 iaceaecomplex 589.4013 590.4083 591.4184 592.4214 Comamo Acidovorax Acidovoraxtemperans 2 adaceae Comamonas Comamonas kerstersii 2 520.3044 Comamonassp 1 Delftia Delftia acidovorans 4 Delftia dentocariosa 1 Delftia sp 2Sutterellaceae Sutterella Sutterella 2 wadsworthensis NeisseriaceaeEikenella Eikenella corrodens 1 Kingella Kingella kingae 3 Kingella sp 1Neisseriales Neisseria Neisseria cineria 1 494.3855 Neisseria elongata 2502.3674 Neisseria flavescens 3 526.3673 Neisseria gonorrhoea 4 527.3704Neisseria lactamica 3 528.3653 Neisseria meningitidis 4 544.3774Neisseria mucosa 2 Epsilon- Campylo- Campylo- Campylo- Campylobactercoli 1 Proteobacteria bacterales bacteraceae bacter Campylobacter fetus3 730.5422 867.6582 Campylobacter jejuni 3 731.5452 993.8381Campylobacter sp 6 867.6582 Helico- Helicobacter Helicobacter pylori 3993.8381 bacteraceae 271.2284 272.2305 299.2595 300.2625 400.2644543.4623 544.4634 Gamma- Aero- Aero- Aeromonas Aeromonas hydrophila 1Proteobacteria monadales monadaceae Cardio- Cardio- Cardio-Cardiobacterium 4 bacteriales bacteriaceae bacterium hominis 648.4603649.4623 650.4653 793.4792 794.4802 Entero- Entero- CitrobacterCitrobacter amalonaticus 1 bacteriales bacteriaceae Citrobacter braakii3 702.5083 Citrobacter freundii 4 703.5092 Citrobacter koseri 4 993.7282Enterobacter Enterobacter absuriae 2 994.7272 Enterobacter aerogenes 3Enterobacter amnigenus 1 Enterobacter cloacae 3 Enterobacter gergoviae 1Escherichia Escherichia coli 7 Hafnia Hafnia alvei 3 Hafnia paralvei 2Hafnia sp 1 Klebsiella Klebsiella oxytoca 5 Klebsiella pneumoniae 5Morganella Morganella morganii 7 Panthoea Panthoea sp 1 Proteus Proteusmirabilis 5 Proteus vulgaris 5 Provedencia Provedencia rettgeri 2Provedencia stuartii 2 Raoultella Raoultella ornithololytica 1Raoultella planticola 1 Salmonella Salmonella poona 1 Serratia Serratialiguifaciens 3 Serratia marcescens 5 Shigella Shigella sonnei 1Pasteurellales Pasteurellaceae Aggregati- Aggregatibacter 5 690.4983bacter aphrophilus 746.4503 Haemophilus Haemophilus influenzae 5823.5453 Haemophilus 2 898.6921 parahaemolyticus 1 915.6902 Haemophilus977.7282 parainfluenzae Pasteurella Pasteurella multocida 2 Pseudo-Moraxellaceae Acineto- Acinetobacter baumanii 5 monadales bacterAcinetobacter iwoffii 5 Acinetobacter johnsonii 2 Acinetobacter junii 1Moraxella Moraxella catarrhalls 5 Moraxella osloensis 2 Pseudo- Pseudo-Pseudomonas 7 monadaceae monas aearuginosa 1 286.1805 Pseudomonasluteola 2 490.3304 Pseudomonas monteilii 2 514.3294 Pseudomonas 1oryzihabitans 5 Pseudomonas putida Pseudomonas stutzeri VibrionalesVibrionaceae Vibrio Vibrio alginolyticus 1 605.3823 Vibrio cholerae 1607.3983 Vibrio furnissii 1 608.4013 633.4134 Xantho- Xantho- Stenotro-Stenotrophomonas 7 monadales monadaceae phomonas maltophilia 377.2105562.3504 619.4353 620.4384 705.4713 706.4743 929.6852 930.6892 942.6912943.7012 944.7052 Gram Actino- Actinobacteria Actino- Actino- Actino-Actinobaculum schaalii 2 posi- bacteria mycetales mycetaceae baculumtive 757.5403 Actinomyces Actinomyces graevenitzii 1 879.6112Actinomyces israelii 1 Actinomyces 2 odontolyticus 5 Actinomyces oris 1Actinomyces sp 1 Actinomyces turicensis 2 Actinomyces viscosis Coryne-Coryne- Corynebacterium 2 bacteriaceae bacterium afermentans 3 493.4624Corynebacterium 2 495.4784 amycolatum 3 497.4845 Corynebacterium 1521.4934 diphtheriae 5 535.4734 Corynebacterium imitans 3 537.4904Corynebacterium 538.4934 minutissimum Corynebacterium sp Corynebacteriumstriatum Micro- Micro- Microbacterium sp 1 bacteriaceae bacterium Myco-Myco- Mycobacterium avium 2 bacteriaceae bacterium Mycobacterium 1391.3684 fortuitum 1 427.0965 Mycobacterium 724.8873 peregrium 817.4152850.5592 851.5662 852.5672 Nocardiaceae Nocardia Nocardia sp 1 321.2915Rhodococcus Rhodococcus egui 1 743.7273 Rhodococcus sp 2 771.7592797.7762 800.7962 827.8162 828.8222 970.7871 Propioni- Propioni-Propionibacterium 7 bacteriaceae bacterium acnes 361.2155 617.4564713.4752 714.4812 779.5072 877.5592 906.5872 Bifido- Bifido- Bifido-Bifidobacterium 1 bacteriales bacteriaceae bacterium adolescentis 2789.5293 Bifidobacterium bifidum 3 792.5502 Bifidobacterium breve 1819.5783 Bifidobacterium infantis 3 830.5622 Bifidobacterium longum 2855.5272 Bifidobacterium 884.6092 pseudocatenulatum 885.6142 GardnerellaGardnerella vaginalis 2 Micro- Micrococcaceae Arthrobacter Arthrobacter1 coccales 914.5711 creatinolyticus 1 913.5682 915.5671 Arthrobacter sp913.5682 Kokuria Kokuria kristina 2 Kokuria rhizophila 2 Kokuria varians1 Micrococcus Micrococcus luteus 5 Micrococcus lylae 2 Rothia Rothiaaeria 3 Rothia amarne 1 Rothia dentocariosa 5 Rothia mucilaginosa 5Rothia sp 1 Micrococcineae Brevi- Brevibacterium 1 bacterium paucivorans3 Brevibacterium sp Dermabacter Dermabacter hominis 2 Dermobacter sp 1Firmi- Bacilli Bacillales Bacillaceae Bacillus Bacillus cereus 3 cutesBacillus clausii 3 Bacillus lichenformis 3 Bacillus pumilus 1 Bacillussonorensis 1 Bacillus sp 3 Bacillus subtilis 3 Listeriaceae ListeriaListeria monocytogenes 7 675.9793 832.5352 Paeni- PaenibacillusPaenibacillus sp 5 bacillaceae Paenibacillus unalis 1 871.5892 903.7221914.7282 915.7282 916.7282 Staphylo- Staphyl- Staphylococcus aureus 3coccaceae ococcus Staphylococcus capitis 3 763.5512 Staphylococcuscaprae 1 765.5482 Staphylococcus cohnii 4 Staphylococcus 3 epidermis 3Staphylococcus 3 haemolyticus 3 Staphylococcus hominis 3 Staphylococcus3 lugdunensis 3 Staphylococcus pasteuri 3 Staphylococcus pettenkoferiStaphylococcus saprophyticus Staphylococcus warneri AerococcaceaeAbiotrophia Abiotrophia defectiva 1 163.0506 Aerococcus Aerococcus sp 1Aerococcus viridans 2 Carno- Granuli- Granulicatella adiacens 1bacteriaceae catella Entero- Enterococcus Enterococcus avium 3 coccaceaeEnterococcus 2 casseliflavus 1 Enterococcus cecorum 3 Enterococcusfaecalis 3 Enterococcus faecium 3 Enterococcus gallinarum 3 Enterococcusraffinosus Lacto- Lactococcus Lactococcus lactis 1 bacillaceaeLactococcus spp 2 Leucono- Leuconostoc Leuconostoc sp 1 stocaceaeLactobacillus Lactobacillus gasseri 2 Lactobacillus rhamnosus 3 Lacto-Strepto- Strepto- Streptococcus agalactiae 3 bacillales coccaceae coccusStreptococcus anginosus 3 898.5391 897.5351 Streptococcus bovis 3923.5512 Streptococcus canis 1 925.5671 Streptococcus 2 926.5701constellatus 2 928.5952 Streptococcus cristatus 3 949.5672 Streptococcus3 950.5692 dysagalactiae 3 951.5832 Streptococcus 3 952.5861gallolyticus 3 953.5981 Streptococcus gordonii 3 954.6011 Streptococcus3 955.5971 intermedius 3 956.5971 Streptococcus lutetiensis 3 979.6111Streptococcus milieri 3 990.6001 Streptococcus mitis 3 Streptococcusmutans 1 Streptococcus oralis 2 Streptococcus 3 parasanguinus 3Streptococcus 3 pneumoniae 1 Streptococcus povas 3 Streptococcuspseudoporcinus Streptococcus pyogenes Streptococcus salivariusStreptococcus sanguinis Streptococcus vestibularis Streptococcusviridans Clostridia Clostridiales Clostridiaceae Clostridium Clostridium1 449.2685 649.4453 celerecrescens 4 703.4923 731.5253 Clostridiumdifficile 2 704.4953 897.6951 Clostridium histolyticum 3 731.5253925.7262 Clostridium innocuum 2 732.5283 969.7481 Clostridium 3 925.7262970.7541 paraputrificum 3 Clostridium perfringens 2 Clostridium ramosum2 Clostridium septicum 3 Clostridium sporogenes Clostridium tertiumParvinomas Parvinomas micra 1 Peptostrepto- Peptoni- Peptoniphilus harei5 coccaceae philus 496.4124 497.4214 498.4244 635.3944 645.4133 646.4173681.3923 Negativicutes Seleno- Acidamino- Acida- Acidaminococcus 2423.3505 monadales coccaceae minocoecus fermentans 425.3644 627.4403426.3674 643.4343 461.3394 644.4383 560.4194 730.4652 851.7352 734.5933831.5902 977.6971 978.6931 Veillonellaceae Dialister Dialister sp 1218.1855 Veillonella Veillonella atypica 1 229.1815 Veillonella dispar 1358.2145 Veillonella parvula 1 364.2495 Veillonella ratti 1 655.4713

TABLE 16 Taxon-specific markers as determined on phylum-level.Phylogenetic information Taxonomic level m/z value Compound IDGram-negatives Bacteroidetes 381.2765 spingolipid (Phylum) 653.5113Isotope m/z = 653 654.5143 CerP(d34:1)) 623.5024 isotope m/z = 623640.4993 isotope m/z = 635 639.4954 isotope m/z = 590 393.2764Cer(d18:0/h17:0) 616.4724 isotope m/z = 604 624.5054 isotope m/z = 604637.5044 Cer(d34:0(2OH) 592.4883 isotope m/z = 590 604.5083 PE-DHC605.5113 PE-DHC 606.5033 PE-DHC 590.4923 591.4963 705.5562 691.5395677.5238 Fusobacteria 646.4833 PE plasmalogen (Phylum) 227.2015 PEplasmalogen 648.4832 combinatorial marker 856.6782 with m/z = 227865.6632 696.4953 714.5492 673.4443 644.4652 884.7083 645.4633 647.4812Proteobacteria 768.5182 782.5342 783.5293 Gram-positives Actinobacteria— Firmicutes —

TABLE 17 Taxon-specific markers as determined on class-level.Phylogenetic information Taxonomic level m/z value Compound IDGram-negatives 635.5004 sphingolipid  ^(L)Bacteroidetes Bacteroidetes616.5094 Cer(d36:1(2OH)) 628.4913 636.5044 627.4883 PE-Cer(33:1)644.5033 711.5902 CerP(d36:1) 618.5233 Cer(d36:0(2OH)) 712.5933 619.5273isotope 618 697.5743 DG(42:5) 620.5184 698.5763 648.5003 637.5044617.5124 isotope m/z = 616 Flavobacteria 333.2084 390.2324 566.4794567.4834 568.4864 556.4034 600.4664 565.4654 553.4674 392.2484 651.4953618.4773 619.4813 324.2545 620.4883 393.2504 891.7411 554.4714 552.4643553.4674 651.4953 601.4723 Gram-negatives Fusobacteria (class) ^(L)Fusobacteria Gram-negatives  ^(L)ProteobacteriaAlpha-Proteobacteria Beta-Proteobacteria — Epsilon-Proteobacteria993.8381 867.6582 731.5452 730.5422 Gamma-Proteobacteria —Gram-positives Actinobacteria (class) —  ^(L)ActinobacteriaGram-positives Bacilli —  ^(L)Firmicutes Clostridia 731.5253 PGplasmalogen 732.5283 Isotope m/z = 731 449.2685 703.4923 PG plasmalogen925.7262 704.4953 Isotope m/z = 703 Negativicutes 560.4194 426.3674Isotope m/z = 425 425.3644 423.3505 461.3394 851.7352

TABLE 18 Taxon-specific markers as determined on order-level.Phylogenetic information Taxoromic level m/z value Compound IDGram-negatives  ^(L)Bacteroidetes   ^(L)Bacteroidetes BacteroidalesGram-negatives  ^(L)Bacteroidetes   ^(L)Flavobacteria FlavobacterialesGram-negatives  ^(L)Fusobacteria   ^(L)Fusobacteria FusobacterialesGram-negatives  ^(L)Proteobacteria   ^(L)Alpha-Proteobacteria 795.5572Caulobacterales 797.5723 769.5502 770.5562 957.6261 771.5582 818.5673Rhizobiales 739.5313 784.5902 785.5932 Isotope m/z = 784 439.4155440.4195 Isotope m/z = 439 799.5132 Rhodospiralles 733.5752 734.5753729.5813 733.6173 722.5753 662.5393 747.6283 757.6173 Gram-negativesBurkholderiales —  ^(L)Proteobacteria Neisseriales  ^(L)Beta-Proteobacteria 526.3673 527.3704 Isotope m/z = 526 502.3674544.3774 494.3855 528.3653 Gram-negatives Campylobacterales — ^(L)Proteobacteria   ^(L)Epsilon-Proteobacteria Gram-negatives ^(L)Proteobacteria   ^(L)Gamma-Proteobacteria AeromonadalesCardiobacterales 648.4603 649.4623 Isotope m/z = 648 793.4792 650.4653794.4802 703.5092 Enterobacteriales 702.5083 Isotope m/z = 702 993.7282994.7272 Pasteurellales 746.4503 915.6902 823.5453 898.6921 690.4983977.7282 Pseudomonadales — Vibrionales 607.3983 608.4013 Isotope m/z =607 633.4134 605.3823 562.3504 Xanthomonadales 377.2105 619.4353620.4384 Isotope m/z = 619 930.6892 Isotope m/z = 629 929.6852 944.7052Isotope m/z = 643 943.7012 942.6912 706.4743 Isotope m/z = 705 705.4713PG(31:1) Gram-positives Actinomycetales —  ^(L)ActinobacteriaBifidobacteriales 792.5502   ^(L)Actinobacteria 819.5783 884.6092885.6142 789.5293 830.5622 855.5272 Micrococcales 913.5682Gram-positives Bacillales  ^(L)Firmicutes Lactobacillales   ^(L)Bacilli951.5832 954.6011 952.5861 953.5981 925.5671 956.5971 955.5971 926.5701950.5692 949.5672 928.5952 990.6001 923.5512 898.5391 979.6111Clostridiales Selemonadales

TABLE 19 Taxon-specific markers as determined on family-levelPhylogenetic information Taxonomic level m/z value Compound IDGram-negatives  ^(L)Bacteroidetes   ^(L)Bacteroidetes Bactercidaceae820.7522    ^(L)Bacteroidales Porphyromonadaceae 841.6942 isotope m/z =840 840.6842 948.7562 isotope m/z = 946 949.7592 isotope m/z = 946947.7502 isotope m/z = 946 946.7472 SubPG DHC 945.7372 isotope m/z = 944944.7342 SubPG DHC 933.7362 isotope m/z = 932 932.7332 SubPG DHC872.7072 815.7112 isotope m/z = 814 814.7063 858.6972 934.7422 962.7691isotope m/z = 960 960.7611 SubPG DHC 961.7661 isotope m/z = 960 828.7232829.7262 isotope m/z = 828 854.7022 959.7501 isotope m/z = 958 958.7461921.7912 918.7191 843.7432 910.7471 908.7401 909.7431 Prevotellaceae661.5283 908.7401 675.5453 922.7552 923.7612 676.5503 870.8002Rikenellaceae Gram-negatives  ^(L)Bacteroidetes   ^(L)Flavobacteria   ^(L)Flavobacteriales Flavobacteriaceae Gram-negatives ^(L)Fusobacteria   ^(L)Fusobacteria    ^(L)FusobacterialesFusobacteriaceae Gram-negatives  ^(L)Proteobacteria  ^(L)Alpha-Proteobacteria    ^(L)Caulobacterales CaulobacteraceaeGram-negatives  ^(L)Proteobacteria   ^(L)Alpha-Proteobacteria   ^(L)Rhizobiales Rhizobiaceae Gram-negatives  ^(L)Proteobacteria  ^(L)Alpha-Proteobacteria    ^(L)Rhodospiralles AcetobacteraceaeGram-negatives Alcaligenaceae —   Proteobacteria Burkholderiaceae   ^(L)Beta-Proteobacteria 589.4013   ^(L)Burkholderiales 591.4184590.4083 Isotope 592.4214 m/z = 589 Isotope m/z = 591 Comamonadaceae520.3044 Sutterellaceae — Gram-negatives  ^(L)Proteobacteria  ^(L)Beta-Proteobacteria    ^(L)Neisseriales NeisseriaceaeGram-negatives Campylobacteraceae  ^(L)Proteobacteria 993.8381  ^(L)Epsilon- 867.6582 Proteobacteria Helicobacteriaceae 299.2595C18:0(+O) 300.2625 Isotope ^(L)Campylobacterales 272.2305 m/z = 299271.2284 Isotope 543.4623 m/z = 271 C16:0(+O) 400.2644 544.4634Gram-negatives  ^(L)Proteobacteria   ^(L)Gamma-Proteobacteria   ^(L)Cardiobacterales Cardiobacteriaceae Gram-negatives ^(L)Proteobacteria   ^(L)Gamma-Proteobacteria    ^(L)EnterobacteralesEnterobacteriaceae Gram-negatives  ^(L)Proteobacteria  ^(L)Gamma-Proteobacteria    ^(L)Pasteurellales PasteurellaceaeGram-negatives Moraxellaceae —  ^(L)Proteobacteria Pseudomonadaceae  ^(L)Gamma- 514.3294 Proteobacteria 490.3304    ^(L)Pseudomonadales286.1805 Gram-negatives  ^(L)Proteobacteria   ^(L)Gamma-Proteobacteria   ^(L)Vibrionales Vibrionaceae Gram-negatives  ^(L)Proteobacteria  ^(L)Gamma-Proteobacteria    ^(L)Xanthomonadales XanthomonadaceaeGram-positives Actinomyceteae  ^(L)Actinobacteria 757.5403  ^(L)Actinobacteria    ^(L)Actinomycetales Combinatorial 879.6112markers Corynebacteriaceae 537.4904 Mycolic acid 538.4934 C35:0 535.4734Isotope 493.4624 m/z = 537 495.4784 Mycolic acid 497.4845 C35:1 521.4934Mycolic acid C32:1 Mycolic acid C32:0 Isotope m/z = 495 Mycolic acidC34:1 Microbacteriaceae Mycobacteriaceae 851.5662 PI(35:0) 852.5672Isotope 850.5592 m/z = 851 391.3684 724.8873 427.0965 817.4152Nocardiaceae 798.7762 Isotope m/z = 797 797.7762 Mycolic acid 828.8222C54:3 970.7871 Isotope m/z = 827 321.2915 827.8162 combinatorial800.7962 Mycolic acid C56:2 743.7273 Isotope Mycolic 771.7592 acid C54:2Mycolic acid C50:2 Mycolic acid C52:2 Propionibacteriaceae 617.4564906.5872 779.5072 714.4812 361.2155 713.4752 877.5592 Gram-positivesBifidobacteriaceae  ^(L)Actinobacteria 792.5502   ^(L)Actinobacteria   ^(L)Bifidobacteriales 819.5783 Gram-positives Micrococcaceae ^(L)Actinobacteria 913.5682   ^(L)Actinobacteria 914.5711 Isotope m/z =913    ^(L)Micrococcales 915.5671 Micrococcineae Gram-positivesBacillaceae  ^(L)Firmicutes Listeriaceae   ^(L)Bacilli 675.9793   ^(L)Bacillales 832.5352 Paenibacillaceae 915.7282 916.7282 914.7282871.5892 903.7221 Staphylococcaceae 765.5482 Isotope m/z = 763 763.5512PG(35:0) Gram-positives Aerococcaceae  ^(L)Firmicutes 163.0506  ^(L)Bacilli Carnobacteriaceae    ^(L)Lactoacillales Enterococcaceae —Lactobacillaceae — Leuconostocaceae Streptococcaceae 897.5351Gram-positives Clostridiaceae  ^(L)Firmicutes 731.5253   ^(L)Clostridia970.7541    ^(L)Clostridiales 649.4453 897.6951 969.7481 925.7262Peptostreptococcaceae 497.4214 498.4244 Isotope m/z = 497 681.3923635.3944 496.4124 645.4133 646.4173 Isotope m/z = 645 Gram-positivesAcidaminococcaceae  └Firmicutes 730.4652   └Negativicutes 627.4403   └Selemonadales 831.5902 977.6971 978.6931 643.4343 644.4383 734.5933Veillonellaceae 229.1815 218.1855 364.2495 655.4713 358.2145

1. A method of analysis using mass spectrometry and/or ion mobilityspectrometry comprising: automatically sampling a target comprising orconsisting of a tissue sample using a first device to generate smoke,aerosol or vapour from said target; adding a matrix to said aerosol,smoke or vapour, wherein the matrix is an organic solvent; causing saidaerosol, smoke or vapour, or analyte therein, to impact upon a collisionsurface located within a vacuum chamber of a spectrometer so as togenerate a plurality of analyte ions; mass analysing and/or ion mobilityanalysing said analyte ions in order to obtain spectrometric data; andanalysing said spectrometric data in order to detect necrosis in saidtarget.
 2. A method as claimed in claim 1, wherein said step of usingsaid first device to generate aerosol, smoke or vapour from said targetfurther comprises irradiating said target with a laser.
 3. A method asclaimed in claim 1, wherein said necrosis is selected from the groupconsisting of coagulative necrosis, liquefactive necrosis, caseousnecrosis, fat necrosis, fibrinoid necrosis and gangrenous necrosis.
 4. Amethod as claimed in claim 1, wherein said tissue is adrenal glandtissue, appendix tissue, bladder tissue, bone, bowel tissue, braintissue, breast tissue, bronchi, ear tissue, oesophagus tissue, eyetissue, endometrioid tissue, gall bladder tissue, genital tissue, hearttissue, hypothalamus tissue, kidney tissue, large intestine tissue,intestinal tissue, larynx tissue, liver tissue, lung tissue, lymphnodes, mouth tissue, nose tissue, pancreatic tissue, parathyroid glandtissue, pituitary gland tissue, prostate tissue, rectal tissue, salivarygland tissue, skeletal muscle tissue, skin tissue, small intestinetissue, spinal cord, spleen tissue, stomach tissue, thymus gland tissue,trachea tissue, thyroid tissue, ureter tissue, urethra tissue, soft andconnective tissue, peritoneal tissue, blood vessel tissue and/or fattissue.
 5. A method as claimed in claim 1, wherein said target isaffected by or is in the vicinity of cancer.
 6. A method as claimed inclaim 1, wherein said tissue comprises cancerous tissue.
 7. A method asclaimed in claim 6, wherein said cancerous tissue is grade I, grade II,grade III or grade IV cancerous tissue; metastatic cancerous tissue;mixed grade cancerous tissue; a sub-grade cancerous tissue; healthy ornormal tissue; or cancerous or abnormal tissue.
 8. A method as claimedin claim 1, wherein said matrix is selected from the group consistingof: one or more alcohols; isopropanol; acetone; acetonitrile;tetrahydrofuran; ethyl acetate; ethylene glycol; dimethyl sulfoxide; analdehyde; a ketone; non-polar molecules; hexane; and chloroform.
 9. Amethod according to claim 8, wherein said alcohol is selected frommethanol; ethanol; isopropanol; butanol; and propanol.
 10. A methodaccording to claim 9, wherein said alcohol is isopropanol.
 11. A methodaccording to claim 1, wherein analysing said spectrometric datacomprises analysing the phospholipid composition of said target.
 12. Amethod according to claim 11, wherein said phospholipid compositioncomprises the glycerophospholipid content and/or a variety of lowermolecular weight sphingolipid-derived in the mass range of m/z=500-700.13. A method as claimed in claim 1, wherein analysing said spectrometricdata comprises analysing one or more sample spectra so as to classifysaid aerosol, smoke or vapour sample.
 14. A method as claimed in claim13, wherein analysing said one or more sample spectra so as to classifysaid aerosol, smoke or vapour sample comprises performing unsupervisedanalysis of said one or more sample spectra and/or supervised analysisof the one or more sample spectra.
 15. A method as claimed in claim 13,wherein analysing said one or more sample spectra so as to classify theaerosol, smoke or vapour sample comprises using one or more of: (i)univariate analysis; (ii) multivariate analysis; (iii) principalcomponent analysis (PCA); (iv) linear discriminant analysis (LDA); (v)maximum margin criteria (MMC); (vi) library-based analysis; (vii) softindependent modelling of class analogy (SIMCA); (viii) factor analysis(FA); (ix) recursive partitioning (decision trees); (x) random forests;(xi) independent component analysis (ICA); (xii) partial least squaresdiscriminant analysis (PLS-DA); (xiii) orthogonal (partial leastsquares) projections to latent structures (OPLS); (xiv) OPLSdiscriminant analysis (OPLS-DA); (xv) support vector machines (SVM);(xvi) (artificial) neural networks; (xvii) multilayer perceptron;(xviii) radial basis function (RBF) networks; (xix) Bayesian analysis;(xx) cluster analysis; (xxi) a kernelized method; and (xxii) subspacediscriminant analysis; (xxiii) k-nearest neighbours (KNN); (xxiv)quadratic discriminant analysis (QDA); (xxv) probabilistic principalcomponent Analysis (PPCA); (xxvi) non negative matrix factorisation;(xxvii) k-means factorisation; (xxviii) fuzzy c-means factorisation; and(xxix) discriminant analysis (DA).
 16. Apparatus comprising: a devicefor automatically sampling a target comprising or consisting of a tissuesample, wherein said device comprises a first device for generatingsmoke, aerosol or vapour from said target; a device for adding a matrixto said aerosol, smoke or vapour, wherein said matrix is an organicsolvent; a collision surface located within a vacuum chamber of aspectrometer, wherein in use said aerosol, smoke or vapour or analytetherein is caused to impact upon said collision surface so as togenerate a plurality of analyte ions; a mass analyser and/or ionmobility analyser for analysing said analyte ions in order to obtainspectrometric data; and a processor adapted to analyse saidspectrometric data in order to detect necrosis in said target. 17.Apparatus as claimed in claim 16, wherein said first device comprises alaser for irradiating said target.